Glycaemic Index Values Of Foods Research Articles

Healthy Eating and Active Living for Diabetes-Glycemic Index (HEALD-GI): A Pragmatic Randomized Controlled Trial

Abstract Objectives Rigorous evidence is needed to support uptake of recommendations to include low glycemic index (GI) foods in daily meal planning as an effective dietary self-care strategy for people with type 2 diabetes (T2D). We evaluated the effectiveness of a 12-week web-based GI-targeted nutrition education intervention on dietary intakes and GI-related knowledge among adults with T2D. Methods Participants were randomized to a control group (n = 34) that received standard printed copies of Canada's Food Guide and Diabetes Canada's GI resources or an intervention group (n = 33) that received those same materials, plus an online platform with six self-directed learning modules and supplementary print material. Each module consisted of a customized video, links to reliable websites, chat rooms, and quizzes. Evidence-based GI concept information included GI values of foods and advice for low-GI shopping, recipes, and cooking tips by a Registered Dietitian. Preferred supports through email, text messaging, phone calls, or postal mail to reinforce participants’ learning were also provided. The primary outcome, average daily dietary GI intake, was assessed using 3-day food records. Additional measures including GI knowledge and self-efficacy, glycated hemoglobin A1c, lipids, systolic blood pressure, body mass index, waist circumference, and computer proficiency, were assessed at baseline and at three months post-intervention. Results Participants (N = 67) were 64% men; mean (standard deviation [SD]) age 69.5 (9.3) years, with mean diabetes duration of 19.0 (13.7) years, BMI 30.1 (5.7) kg/m2 and A1c 7.1 (1.2)% at baseline. Mean daily GI intake decreased in the intervention group by 2.79 (7.77) compared to a 0.76 (6.48) increase in the control group (adjusted mean difference [95% CI]; −3.77 [−6.95, −0.58]). Mean GI knowledge 2.14 [0.59, 3.69], understanding of GI concept 1.65 [0.85, 2.44] and self-efficacy for consuming low-GI foods 1.29 [0.51, 2.07] increased among the intervention group (P < 0.01) compared with the control group. Conclusions Web-based GI-targeted education program improved the quality of carbohydrate consumption among adults with T2D and may have been mediated through increases in knowledge and self-efficacy. Web-based nutrition education may be an effective alternative in this population. Funding Sources Canadian Foundation for Dietetic Research.

Glycemic Index and Insulinemic Index of Foods: An Interlaboratory Study Using the ISO 2010 Method

An official method for determining food glycemic index (GI) was published by the Organization for International Standardization (ISO) in 2010, but its performance has not been assessed. Therefore, we aimed to determine the intra- and inter-laboratory variation of food GI values measured using the 2010 ISO method. Three laboratories (Australia, Canada and France) determined the GI and insulinemic-index (II) of six foods in groups of 13–15 participants using the 2010 ISO method and intra- and inter-laboratory Standard Deviations (SDs) were calculated. Overall mean food GIs varied from 47 to 86 (p < 0.0001) with no significant difference among labs (p = 0.57) and no food × laboratory interaction (p = 0.20). Within-laboratory SD was similar among foods (range, 17.8–22.5; p = 0.49) but varied among laboratories (range 17.5–23.1; p = 0.047). Between-laboratory SD of mean food GI values ranged from 1.6 to 6.7 (mean, 5.1). Mean glucose and insulin responses varied among foods (p < 0.001) with insulin (p = 0.0037), but not glucose (p = 0.054), varying significantly among labs. Mean II varied among foods (p < 0.001) but not among labs (p = 0.94). In conclusion, we found that using the 2010 ISO method, the mean between-laboratory SD of GI was 5.1. This suggests that the ISO method is sufficiently precise to distinguish a mean GI = 55 from a mean GI ≥ 70 with 97–99% probability.

The effect of consumption of low glycemic index, high fat content bread on anthropometric measurement and cardiometabolic risk factors in women with type 2 diabetes mellitus

The aim of the present study was to examine the effects of consumption of breads with different glycemic index as part of a balanced diabetic diet on anthropometric and biochemical parameters in patients with type 2 diabetes mellitus. The study was conducted on 24 diabetic women who consulted the Dilovasi State Hospital Medicine Polyclinic; the patients had been diagnosed with type 2 diabetes with body mass index (BMI) ≥ 25 kg/m2 and were aged 40.29 ± 6.81 years. A total of 24 subjects with type 2 diabetes were randomized to consume one of two breads as part of their diets, consuming whole grain bread or wheat bread for 4 weeks. Both groups’ macronutrient distribution was planned so that about 45–65% of their calorie intake was from carbohydrates, 25–35% was from fat, and 15–20% was from protein. All food with glycemic response except bread was suggested similarly for both groups. Nutritional status, glycemic index values of foods, anthropometric values (weight, waist circumference, BMI, etc.), body composition, and biochemical analysis [fasting plasma glucose, fasting insulin, HbA1c, HOMA-IR, total cholesterol, triglycerides, LDL cholesterol, HDL cholesterol, and C-reactive protein (CRP)] were measured at the beginning and the end of the study. Decrease in the average daily glycemic index in the whole grain bread group was 33.45 ± 9.72%, while it was 21.79 ± 5.97% in the bread made from whole wheat flour. The decrease in glycemic index was statistically significant. There was a significant reduction in body weight, BMI, and waist circumference in both groups, but differences between groups were not significant (p > 0.05). A statistically significant, positive relationship was found between decrease in glycemic index and decrease in waist circumference (r(22) = 0.46, p = 0.025). While there was a significant decrease in HbA1c in patients who consume whole grain bread, there was a significant decrease in insulin and HOMA-IR in those who consume wheat bread. There were no statistically significant changes in plasma glucose, HDL cholesterol, total cholesterol, and triglyceride levels (p > 0.05). There is a need to further research to determine the effects of breads with different glycemic indices on glycemic control in patients with type 2 diabetes.

Does the ingestion of a 24\xa0hour low glycaemic index Asian mixed meal diet improve glycaemic response and promote fat oxidation? A controlled, randomized cross-over study

BackgroundThe health benefits of consuming a low glycaemic index (GI) diet to reduce the risk of type 2 Diabetes are well recognized. In recent years the GI values of various foods have been determined. Their efficacy in constructing and consuming a low GI diet over 24 h in modulating glycaemic response has not been fully documented. The translation of using single-point GI values of foods to develop a 24 h mixed meal diet can provide valuable information to consumers, researchers and dietitians to optimize food choice for glycaemic control. By using GI values of foods to develop mixed meals, our study is the first to determine how both blood glucose and substrate oxidation may be modulated over 24 h.MethodsThe study included 11 Asian men with a BMI between 17–24 kg/m2 who followed both a 1-day low GI and 1-day high GI diet in a randomized, controlled cross-over design. Test meals included breakfast, lunch, snack and dinner. Glycaemic response was measured continuously for over 24 h and postprandial substrate oxidation for 10 h inside a whole body calorimeter.ResultsThe low GI diet resulted in lower 24 h glucose iAUC (860 ± 440 vs 1329 ± 614 mmol/L.min; p = 0.014) with lower postprandial glucose iAUC after breakfast (p < 0.001), lunch (p = 0.009), snack (p = 0.012) and dinner (p = 0.003). Moreover, 24 h mean amplitude of glycaemic excursion was lower during the low GI vs high GI diet (1.44 ± 0.63 vs 2.33 ± 0.82 mmol/L; p < 0.001). Simultaneously, decrease in 10 h fat oxidation was less during the low vs high GI diet (−0.033 ± 0.021 vs −0.050 ± 0.017 g/min; p < 0.001), specifically after breakfast (p < 0.001) and lunch (p < 0.001).ConclusionsOur study corroborates that using low GI local foods to construct a 24 h low GI diet, is able to reduce glycaemic response and variability as recorded by continuous glucose monitoring. Our observations also confirm that a low GI diet promotes fat oxidation over carbohydrate oxidation when compared to a high GI diet. These observations provide public health support for the encouragement of healthier nutrition choices by consuming low GI foods.Trial registrationNCT 02631083 (Clinicaltrials.gov).

Health Canada’s evaluation of the use of glycemic index claims on food labels

The glycemic index (GI) is a system that ranks foods according to the blood glucose-increasing potential of servings of foods that provide the same amount of available carbohydrate. The GI was originally developed as a tool for carbohydrate exchange in the dietary management of glycemia in persons with diabetes, and studies have generally supported modest benefits of low-GI diets in this population. Despite inconsistent results for the utility of the GI in the nondiabetic population, there is some interest in its universal application on food labels to assist consumers in making food choices that would help them meet their dietary goals. The objective of this review was to evaluate the usefulness of including the GI values of foods as part of the information on food labels in Canada. Health Canada's assessment identified 3 areas of concern with respect to GI labeling: 1) the GI measure has poor accuracy and precision for labeling purposes; 2) as a ratio, the GI does not vary in response to the amount of food consumed and the partial replacement of available carbohydrates with unavailable carbohydrates, whereas the glycemic response does; and 3) an unintended focus on the GI for food selection could lead to food choices that are inconsistent with national dietary guidelines. Hence, Health Canada's current opinion is that the inclusion of the GI value on the label of eligible food products would be misleading and would not add value to nutrition labeling and dietary guidelines in assisting consumers to make healthier food choices.

Calculating meal glycemic index by using measured and published food values compared with directly measured meal glycemic index

Glycemic index (GI) testing is normally based on individual foods, whereas GIs for meals or diets are based on a formula using a weighted sum of the constituents. The accuracy with which the formula can predict a meal or diet GI is questionable. Our objective was to compare the GI of meals, obtained by using the formula and by using both measured food GI and published values, with directly measured meal GIs. The GIs of 7 foods were tested in 30 healthy people. The foods were combined into 3 meals, each of which provided 50 g available carbohydrate, including a staple (potato, rice, or spaghetti), vegetables, sauce, and pan-fried chicken. The mean (95% CI) meal GIs determined from individual food GI values and by direct measurement were as follows: potato meal [predicted, 63 (56, 70); measured, 53 (46, 62)], rice meal [predicted, 51 (45, 56); measured, 38 (33, 45)], and spaghetti meal [predicted, 54 (49, 60); measured, 38 (33, 44)]. The predicted meal GIs were all higher than the measured GIs (P < 0.001). The extent of the overestimation depended on the particular food, ie, 12, 15, and 19 GI units (or 22%, 40%, and 50%) for the potato, rice, and spaghetti meals, respectively. The formula overestimated the GI of the meals by between 22% and 50%. The use of published food values also overestimated the measured meal GIs. Investigators using the formula to calculate a meal or diet GI should be aware of limitations in the method. This trial is registered with the Australian and New Zealand Clinical Trials Registry as ACTRN12611000210976.

Are the glycemic and insulinemic index values of carbohydrate foods similar in healthy control, hyperinsulinemic and type 2 diabetic patients?

a criticism of glycemic index (GI) is that it does not indicate the insulin response of foods (insulinemic index, II). However, it is unknown if the GI and II values of foods are equivalent in all subjects, a necessary criterion for clinical utility. We compared GI and II values in non-diabetic subjects with fasting-serum-insulin (FSI) <40 pmol/l (healthy control) or with FSI ≥ 40 pmol/l (hyper[I]) and subjects with type 2 diabetes (T2DM), and to see whether GI and II were related to the serum-glucose concentrations, insulin sensitivity, β-cell function and hepatic insulin extraction (HIE) of the subjects. Serum-glucose, -insulin and -C-peptide responses after 50 g available-carbohydrate portions of glucose (tested three times by each subject), sucrose, instant mashed-potato, white-bread, polished-rice and pearled-barley were measured in healthy control (n=9), hyper[I] (n=12) and T2DM (n=10) subjects. Food GI values did not differ significantly among the three subject groups, whereas II values were higher in T2DM (100±7) than healthy controls (78±5) and hyper[I] subjects (70±5) (mean±s.e.m., P=0.05). II was inversely associated with insulin sensitivity (r=-0.66, P<0.0001) and positively related to fasting- and postprandial-glucose (both r=0.68, P<0.0001) and HIE (r=0.62, P=0.0002). In contrast, GI was not related to any of the biomarkers (P>0.05). The GI is a valid property of foods because its value is similar in healthy control, hyper [I] and T2DM subjects, and is independent of subjects' metabolic status. However, II may depend upon the glycaemic control, insulin sensitivity and HIE of the subjects.

Methodology for adding and amending glycaemic index values to a nutrition analysis package

Since its introduction in 1981, the glycaemic index (GI) has been a useful tool for classifying the glycaemic effects of carbohydrate foods. Consumption of a low-GI diet has been associated with a reduced risk of developing CVD, diabetes mellitus and certain cancers. WISP (Tinuviel Software, Llanfechell, Anglesey, UK) is a nutrition software package used for the analysis of food intake records and 24h recalls. Within its database, WISP contains the GI values of foods based on the International Tables 2002. The aim of the present study is to describe in detail a methodology for adding and amending GI values to the WISP database in a clinical or research setting, using data from the updated International Tables 2008.

Glycaemic index and glycaemic load values of a selection of popular foods consumed in Hong Kong

The objective of the present paper is to provide glycaemic index (GI) and glycaemic load (GL) values for a variety of foods that are commonly consumed in Hong Kong and expand on the international GI table of Chinese foods. Fasted healthy subjects were given 50 g of available carbohydrate servings of a glucose reference, which was tested twice, and test foods of various brands of noodles (n 5), instant cereals (n 3) and breads (n 2), which were tested once, on separate occasions. For each test food, tests were repeated in ten healthy subjects. Capillary blood glucose was measured via finger-prick samples in fasting subjects ( - 5, 0 min) and at 15, 30, 45, 60, 90 and 120 min after the consumption of each test food. The GI of each test food was calculated geometrically by expressing the incremental area under the blood glucose response curve (IAUC) of each test food as a percentage of each subject's average IAUC for the reference food. GL was calculated as the product of the test food's GI and the amount of available carbohydrate in a reference serving size. The majority of GI values of foods tested were medium (a GI value of 56-69) to high (a GI value of 70 or more) and compared well with previously published values. More importantly, our dataset provides GI values of ten foods previously untested and presents values for foods commonly consumed in Hong Kong.

Dietary patterns associated with glycemic index and glycemic load among Alberta adolescents

The purpose of this study was to assess the dietary glycemic index (GI) and glycemic load (GL) of adolescents, based on a Web-based 24-h recall, and to investigate dietary predictors of GI and GL. In addition, the relationship between GI and GL and weight status was examined. A Web-based 24-h recall was completed by 4936 adolescents, aged 9-17 years; macronutrient and food group intakes were assessed using the ESHA Food Processor, the Canadian Nutrient File, and Canada's Food Guide. Dietary GI and GL were calculated based on published GI values for foods. Students provided self-reported height and mass. Multiple regression models assessed the ability of food group choices and food behaviours to predict GI and GL. Mean GI was 55 for girls and 56 for boys. Mean GL was 128 for girls and 168 for boys. Food group choices explained 26% of the variation in GI (p < 0.01) and 84% of the variation in GL (p < 0.01). The number of meals per day explained 10% (p < 0.01), and eating meals outside of the home accounted for 2.5% (p < 0.01) of the variation in GL; however, these results disappeared when adjusted for total energy intake. The GI was positively correlated with body mass index in girls (r = 0.05, p = 0.02), and GL was significantly higher among nonoverweight boys than overweight boys. This study identified eating patterns related to daily GI and GL, and suggests certain dietary patterns that could have beneficial effects on health. It also showed that GI and GL were weakly related to weight status.

The glycaemic index values of foods containing fructose are affected by metabolic differences between subjects

Glycaemic responses are influenced by carbohydrate absorption rate, type of monosaccharide absorbed and the presence of fat; the effect of some of these factors may be modulated by metabolic differences between subjects. We hypothesized that glycaemic index (GI) values are affected by the metabolic differences between subjects for foods containing fructose or fat, but not for starchy foods. The GI values of white bread (WB), fruit leather (FL) and chocolate-chip cookies (CCC) (representing starch, fructose and fat, respectively) were determined in subjects (n=77) recruited to represent all 16 possible combinations of age (< or =40, >40 years), sex (male, female), ethnicity (Caucasian, non-Caucasian) and body mass index (BMI) (< or =25, >25 kg/m2) using glucose as the reference. At screening, fasting insulin, lipids, c-reactive protein (CRP), aspartate transaminase (AST) and waist circumference (WC) were measured. There were no significant main effects of age, sex, BMI or ethnicity on GI, but there were several food x subject-factor interactions. Different factors affected each food's area under the curve (AUC) and GI. The AUC after oral glucose was related to ethnicity, age and triglycerides (r 2=0.27); after WB to ethnicity, age, triglycerides, sex and CRP (r 2=0.43); after CCC to age and weight (r 2=0.18); and after FL to age and CRP (r 2=0.12). GI of WB was related to ethnicity (r 2=0.12) and of FL to AST, insulin and WC (r 2=0.23); but there were no significant correlations for CCC. The GI values of foods containing fructose might be influenced by metabolic differences between -subjects, whereas the GI of starchy foods might be affected by ethnicity. However, the proportion of variation explained by subject factors is small.

Is Glycemic Index of Food a Feasible Predictor of Appetite, Hunger, and Satiety?

This review assesses the feasibility of using glycemic index (GI) as a predictor of appetite, hunger and satiety by surveying published human intervention studies. We also discuss the relationship between GI and two appetite/satiety control hormones, leptin and ghrelin. Ingestion of high-GI food increased hunger and lowered satiety in short-term human intervention studies. This effect may be attributed to the rapid decline in blood glucose level following a hyperinsulinemic response caused by a sharp and transient increase in blood glucose level that occurs after the ingestion of high-GI food, which is defined as the glucostatic theory. However, appetite, hunger and satiety after the ingestion of foods with varying GI were inconsistent among long-term human intervention studies. From the few relevant long-term studies available, we selected two recent well-designed examples for analysis, but they failed to elicit clear differences in glycemic and insulinemic responses between high- and low-GI meals (consisting of a combination of different foods or key carbohydrate-rich foods incorporated into habitual diets). One of the reasons that these studies could not predict glycemic response to mixed meals is presumably that the GI of each particular food was not reflected in that of the mixed meals as a whole. Thus, it is difficult to conclude that the GI values of foods or mixed meals are a valid long-term predictor for appetite, hunger and satiety. Both insulin and insulin-mediated glucose uptake and metabolism in adipose tissue affect blood leptin concentration and its diurnal pattern. Circulating ghrelin level is suppressed by carbohydrate-rich meals, presumably via glycemia and insulinemia. Accordingly, low-GI foods may not necessarily increase satiety or suppress appetite and/or hunger because of the lack of insulin-mediated leptin stimulation and ghrelin suppression. However, insulin-mediated leptin stimulation and ghrelin suppression per se is not consistent among studies; thus we were not able to identify a clear relationship among GI, satietogenic leptin, and appetitic ghrelin.

Measuring the glycemic index of foods: interlaboratory study

BackgroundMany laboratories offer glycemic index (GI) services. ObjectiveWe assessed the performance of the method used to measure GI. DesignThe GI of cheese-puffs and fruit-leather (centrally provided) was measured in 28 laboratories (n = 311 subjects) by using the FAO/WHO method. The laboratories reported the results of their calculations and sent the raw data for recalculation centrally. ResultsValues for the incremental area under the curve (AUC) reported by 54% of the laboratories differed from central calculations. Because of this and other differences in data analysis, 19% of reported food GI values differed by >5 units from those calculated centrally. GI values in individual subjects were unrelated to age, sex, ethnicity, body mass index, or AUC but were negatively related to within-individual variation (P = 0.033) expressed as the CV of the AUC for repeated reference food tests (refCV). The between-laboratory GI values (mean ± SD) for cheese-puffs and fruit-leather were 74.3 ± 10.5 and 33.2 ± 7.2, respectively. The mean laboratory GI was related to refCV (P = 0.003) and the type of restrictions on alcohol consumption before the test (P = 0.006, r2 = 0.509 for model). The within-laboratory SD of GI was related to refCV (P < 0.001), the glucose analysis method (P = 0.010), whether glucose measures were duplicated (P = 0.008), and restrictions on dinner the night before (P = 0.013, r2 = 0.810 for model). ConclusionsThe between-laboratory SD of the GI values is ≈9. Standardized data analysis and low within-subject variation (refCV < 30%) are required for accuracy. The results suggest that common misconceptions exist about which factors do and do not need to be controlled to improve precision. Controlled studies and cost-benefit analyses are needed to optimize GI methodology. The trial was registered at clinicaltrials.gov as NCT00260858.

Development of a Glycemic Index Database for Food Frequency Questionnaires Used in Epidemiologic Studies

Consumption of foods with a high glycemic index (GI) or glycemic load (GL) is hypothesized to contribute to insulin resistance, which is associated with increased risk of diabetes mellitus, obesity, cardiovascular disease, and some cancers. However, dietary assessment of GI and GL is difficult because values are not included in standard food composition databases. Our objective was to develop a database of GI and GL values that could be integrated into an existing dietary database used for the analysis of FFQ. Food GI values were obtained from published human experimental studies or imputed from foods with a similar carbohydrate and fiber content. We then applied the values to the Women's Health Initiative (WHI) FFQ database and tested the output in a random sample of previously completed WHI FFQs. Of the 122 FFQ line items (disaggregated into 350 foods), 83% had sufficient carbohydrate (>5 g/serving) for receipt of GI and GL values. The foods on the FFQ food list with the highest GL were fried breads, potatoes, pastries, pasta, and soft drinks. The fiber content of foods had very little influence on calculated GI or GL estimates. The augmentation of this FFQ database with GI and GL values will enable etiologic investigations of GI and GL with numerous disease outcomes in the WHI and other epidemiologic studies that utilize this FFQ.

Glycaemic index and glycaemic load values of commercially available products in the UK

The objective of this paper is to provide glycaemic index (GI) and glycaemic load (GL) values for a variety of foods that are commercially available in the UK and to compare these with previously published values. Fasted subjects were given isoglucidic (50 or 25 g carbohydrate) servings of a glucose reference at least two to three times, and test foods once, on separate occasions. For each test food, tests were repeated in at least eight subjects. Capillary blood glucose was measured via finger-prick samples in fasting subjects (0 min) and at 15, 30, 45, 60, 90 and 120 min after the consumption of each test food. The GI of each test food was calculated geometrically by expressing the incremental area under the blood glucose response curve (IAUC) of each test food as a percentage of each subject's average IAUC for the reference food. GL was calculated as the product of the test food's GI and the amount of available carbohydrate in a reference serving size. The majority of GI values of foods tested in the current study compare well with previously published values. More importantly, our data set provides GI values of several foods previously untested and presents values for foods produced commercially in the UK.

Testing the glycaemic index of foods: in vivo, not in vitro

The glycaemic index (GI) of foods is receiving increasing coverage in medical and nutrition literature and food manufacturers are showing interest in determining and publicising the GI values of their products. Although an extensive international table of GI values for foods was recently published (Foster-Powell et al, 2002), the need for continued testing and product development is evident. In particular, the wider implementation of a low-GI diet will depend on the identification of low-GI foods using standardised in vivo methodology (FAO/WHO expert consultation, 1998). Research evidence to date confirms that the GI values of foods cannot be accurately predicted from their ingredients, nutrient composition or preparation methods (Truswell, 1992).

Determination of the glycaemic index of foods: interlaboratory study.

Practical use of the glycaemic index (GI), as recommended by the FAO/WHO, requires an evaluation of the recommended method. Our purpose was to determine the magnitude and sources of variation of the GI values obtained by experienced investigators in different international centres. GI values of four centrally provided foods (instant potato, rice, spaghetti and barley) and locally obtained white bread were determined in 8-12 subjects in each of seven centres using the method recommended by FAO/WHO. Data analysis was performed centrally. University departments of nutrition. Healthy subjects (28 male, 40 female) were studied. The GI values of the five foods did not vary significantly in different centres nor was there a significant centrexfood interaction. Within-subject variation from two centres using venous blood was twice that from five centres using capillary blood. The s.d. of centre mean GI values was reduced from 10.6 (range 6.8-12.8) to 9.0 (range 4.8-12.6) by excluding venous blood data. GI values were not significantly related to differences in method of glucose measurement or subject characteristics (age, sex, BMI, ethnicity or absolute glycaemic response). GI values for locally obtained bread were no more variable than those for centrally provided foods. The GI values of foods are more precisely determined using capillary than venous blood sampling, with mean between-laboratory s.d. of approximately 9.0. Finding ways to reduce within-subject variation of glycaemic responses may be the most effective strategy to improve the precision of measurement of GI values.

Glycaemic index of 102 complex carbohydrate foods in patients with diabetes

Low glycaemic index diets reduce blood glucose and lipid levels in humans but glycaemic index values are only available for a small number of foods. Thus, we determined the glycaemic index of 102 complex carbohydrate foods in patients with diabetes. The values varied from 37 for bean thread noodles to 127 for Rice Chex cereal (p<0.001). There were no significant differences between the values of 14 different commercial leavened, wheat breads, which had a mean glycaemic index of 97. This supports the validity of using white bread as the standard food with an arbitrary glycaemic index of 100. There were significant differences between the glycaemic index values of individual foods in the following groups: rye breads, cakes, corn products, cookies, crackers, grains, pasta, potato, soups, legumes and breakfast cereals. Legumes and pasta tended to have low glycaemic index values. The glycaemic index values of the foods were weakly negatively related to their protein (r=−0.407; p<0.001) and dietary fibre (r=0.322; p<0.001) content but not fat (r=−0.054, ns). Thus, there are sufficient differences between the glycaemic responses of complex carbohydrate foods to make the glycaemic index classification a useful supplement to food tables in planning diets for patients with metabolic disorders such as diabetes or hyperlipidaemia.

The use of the glycemie Index in predicting the blood glucose response to mixed meals

There has been much interest in the use of the glycemic index (GI). A recent study reporting plasma glucose responses to mixed meals containing fat and protein concluded that the results were totally disparate from what would have been expected from published GI values of the foods fed. However, this conclusion was based upon an inappropriate assessment of the data using absolute rather than incremental blood glucose response areas. The present report demonstrates how data may be analyzed to make use of the GI values of individual foods to predict the GI of mixed meals (r = 0.987; p < 0.02). It is concluded that the GI concept applies well to mixed meals containing fat and protein.