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Human Nutrition and Metabolism

Physiological Validation of the Concept of Glycemic Load in Lean Young Adults¹

J. C. Brand-Miller^*, M. Thomas^*, V. Swan^*, Z. I. Ahmad^*, P. Petocz and S. Colagiuri^**

^* Human Nutrition Unit, School of Molecular and Microbial Biosciences, University of Sydney; School of Mathematical Sciences, University of Technology; and ^** Department of Endocrinology, Diabetes and Metabolism, Prince of Wales Hospital, Sydney, NSW, Australia

²To whom correspondence should be addressed. E-mail: j.brandmiller{at}staff.usyd.edu.au.

	ABSTRACT

TOP ABSTRACT RESEARCH DESIGN AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Dietary glycemic load, the mathematical product of the glycemic index (GI) of a food and its carbohydrate content, has been proposed as an indicator of the glucose response and insulin demand induced by a serving of food. To validate this concept in vivo, we tested the hypotheses that 1) portions of different foods with the same glycemic load produce similar glycemic responses; and 2) stepwise increases in glycemic load for a range of foods produce proportional increases in glycemia and insulinemia. In the first study, 10 healthy subjects consumed 10 different foods in random order in amounts calculated to have the same glycemic load as one slice of white bread. Capillary blood samples were taken at regular intervals over the next 2 h. The glycemic response as determined by area under the curve was not different from that of white bread for nine foods. However, lentils produced lower than predicted responses (P < 0.05). In the second study, another group of subjects was tested to determine the effects of increasing glycemic load using a balanced 5 x 5 Greco-Latin square design balanced for four variables: subject, dose, food and order. Two sets of five foods were consumed at five different glycemic loads (doses) equivalent to one, two, three, four and six slices of bread. Stepwise increases in glycemic load produced significant and predictable increases in both glycemia (P < 0.001) and insulinemia (P < 0.001). These findings support the concept of dietary glycemic load as a measure of overall glycemic response and insulin demand.

KEY WORDS: • glycemic index • glycemic load • diabetes • carbohydrate • insulin

For Commentary on this article see: J Nutr. 133: 2695–2696, 2003.

It has been hypothesized that the aspects of diet that increase insulin resistance or secretion influence the risk of various diseases, including type 2 diabetes and coronary heart disease (1,2). Although dietary carbohydrate has obvious links, the nature of this relationship is complex (3,4). Higher intake of carbohydrate can increase insulin sensitivity (5,6), yet some types of carbohydrate produce insulin resistance and adverse lipid profiles (7,8). Postprandial hyperglycemia may increase disease risk by increasing oxidative stress and protein glycation (9,10).

In 1997 nutritional epidemiologists at the Harvard School of Public Health introduced the concept of dietary glycemic load to encompass the idea that the overall glycemic effect of a diet, not the amount of carbohydrate alone, may be related to disease risk (11). They defined dietary glycemic load as the product of the glycemic index (GI)² of a food and the amount of carbohydrate in a serving. By summing the glycemic load contributed by individual foods, the overall glycemic load of the whole diet was calculated. This figure, adjusted for total energy intake, was considered to represent the combination of both quantity and quality of dietary carbohydrate intake and their interaction.

In the past five years, the Harvard group and others have reported that overall glycemic load is an independent risk factor for type 2 diabetes in men and women (11,12), cardiovascular morbidity and mortality in women (13) and certain types of cancers in both sexes (14,15). In addition, surrogate risk factors, including HDL cholesterol (16,17), fasting triglyceride concentrations (16) and C-reactive protein levels (18), have been independently correlated with overall glycemic load. The latter findings support the physiological relevance of glycemic load as a potential risk factor for coronary heart disease, particularly in individuals prone to insulin resistance.

Despite the importance of these findings, the concept of glycemic load remains controversial because it is a mathematical concept based on an already controversial approach to classifying foods, the GI. Furthermore, there is no direct evidence that glycemic load has physiological meaning. Each unit of dietary glycemic load can be interpreted as the equivalent of 1 g of carbohydrate from white bread or glucose depending on the reference used in determining the GI (13,16).

To document a direct association between glycemic load and physiological responses, we carried out a series of experiments that tested the inherent assumptions that 1) portions of different foods calculated to have the same glycemic load produce similar blood glucose responses; and 2) stepwise increases in glycemic load produce proportionate increases in both glycemia and insulinemia.

Thus, our specific aims were to compare glucose responses to a wide spectrum of foods fed in portions that have the same calculated glycemic load as one slice of white bread, and to determine the dose-response relationships between the number of portions consumed in one meal and the postprandial glucose and insulin responses.

	RESEARCH DESIGN AND METHODS

TOP ABSTRACT RESEARCH DESIGN AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
A pool of 30 lean healthy volunteers participated in the two studies. They were recruited from the University of Sydney by advertisement and selected on the basis of age (18–40 y) and BMI (19–26 kg/m²). The mean age ± SD of the 30 subjects was 26.5 ± 4.3 y and the mean BMI ± SD was 22.0 ± 2.5 kg/m². None of the subjects was taking medication known to alter glucose metabolism. The study was approved by the institutional human ethics committee and all subjects gave written informed consent.

Study design.

In Study 1, 10 different food portions having the same glycemic load as one slice of white bread were compared. Ten subjects (five male, five female) consumed each food portion on different occasions in random order several days apart. The test foods were selected to provide a wide range of carbohydrate content and GI. The portion sizes were calculated using the formula:

Because white bread has a GI of 70 and 15 g of carbohydrate per slice, the glycemic load of one slice of white bread is (70 x 15)/100 = 10.5 U. We calculated the portion sizes of the test foods so that all had a glycemic load of 10.5 U (Table 1). GI values were used as previously reported from Foster-Powell and Brand-Miller (19). Food tables and manufacturer’s data were used to determine the carbohydrate content per 100 g and to convert the weight of carbohydrate to serving weight.

Testing began between 0630 and 0830 h following a 10-h overnight fast. Two baseline fingerprick capillary blood samples were taken upon arrival and the meal started at time 0. Subjects consumed all food within 12 min. Additional blood samples were taken at 15, 30, 45, 60, 90 and 120 min after the start of the meal. An automated Autoclix lancet device (Boehringer Mannheim, Australia) with deep puncture was used for capillary sampling so that 800 mL of blood could be collected for glucose (Study 1 and 2) and insulin assay (Study 2 only). To enhance peripheral blood circulation to the fingers, subjects warmed their hands in hot water for 5 min before each blood sampling. Blood was collected into 1.5-mL microcentrifuge tubes coated with heparin (10 IU heparin sodium salt; Sigma Chemical, St Louis, MO) and immediately centrifuged at 12,000 x g for 30 s. The plasma was removed and stored at -20°C for later analysis.

Plasma glucose was assayed using a Unimate-5 Glucose Hexokinase kit (Roche Diagnostica, Frenchs Forest, NSW Australia) and a Cobas Fara II Spectrophotometric Autoanalyser (Hoffman-La Roche Diagnostica, Basel, Switzerland). Plasma insulin concentrations were determined using a Coat-a-Count Insulin Radioimmunoassay kit (Diagnostic Products, Los Angeles, CA).

Data analysis.

Results are presented as means ± SEM. Plasma glucose and insulin responses were quantified as the incremental area under the curve (AUC) over 120 min using Simpson’s rule (20). The baseline was defined as the average of the two initial fasting samples and areas below baseline were ignored. In Study 1, the mean of 10 subjects was calculated for each food and compared using two-way ANOVA. With 10 subjects, the study had >80% power to discriminate 1 SD between foods at P < 0.05. When a difference was detected, a post hoc Fisher’s PLSD test for multiple comparisons was applied. In Study 2, the nature of the dose-response relationship was determined using the line of best fit. For Set A, which included the repeat experiment and therefore n = 20, the intraindividual CV was calculated using the difference between the SD divided by the average of the two means.

	RESULTS

TOP ABSTRACT RESEARCH DESIGN AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Study 1.

The glucose responses, assessed as the AUC, to most foods fed at the same glycemic load as one slice of bread did not differ (Fig. 1). However, lentils resulted in an unexpectedly lower response than the other foods (P < 0.05) and basmati rice tended to produce a lower response (P < 0.07). Ice cream produced the highest response but it was significantly different only from lentils (P < 0.05).

View larger version (36K): [in this window] [in a new window]   FIGURE 1 Human subjects received 10 different foods in portion sizes calculated to be equivalent in dietary glycemic load to one slice of white bread. Values are mean AUC ± SEM, n = 10, for each food. Bars without a common letter differ.

Using all the data points available, increasing glycemic load (dose level) affected the glucose response within both sets of food (P < 0.001, Fig. 2). Dose and postprandial glucose response were linearly related (P < 0.001). A slight curve in the data at high dose levels was observed but a quadratic equation did not improve the fit as determined by the pure error lack of fit test. In food Set A, in which the whole experiment was repeated, the intraindividual CV was 54%.

View larger version (24K): [in this window] [in a new window]   FIGURE 2 Human subjects consumed two sets of five foods in a balanced 5 x 5 Greco-Latin square design. Stepwise increases in glycemic load (dose level), equivalent to one, two, three, four and six slices of white bread, correlated significantly with the observed glucose response (mean AUC) within both sets of foods (n = 20 for Set A; n = 10 for Set B; linear trend P < 0.001 for both).

View larger version (20K): [in this window] [in a new window]   FIGURE 3 Human subjects consumed two sets of five foods in a balanced 5 x 5 Greco-Latin square design. Stepwise increases in glycemic load (dose level), equivalent to one, two, three, four and six slices of white bread, correlated significantly with the observed insulin response (mean AUC) for both sets of foods (n = 20 for Set A; n = 10 for Set B; linear trend P < 0.001 for both).

	DISCUSSION

TOP ABSTRACT RESEARCH DESIGN AND METHODS RESULTS DISCUSSION LITERATURE CITED

Wolever and Bolognesi (21) also tested the assumption that both the source and amount of carbohydrate influence glycemia. Using four foods tested at four dose levels, their findings indicated that the GI of the carbohydrate explains a similar amount of the variability in glycemic responses as the amount of carbohydrate and together they account for 90% of the total variability.

Although the calculated glycemic loads were the same in Study 1, there was a deliberate threefold range in carbohydrate content (13 to 40 g) and threefold variation in GI (26–81 on the glucose = 100 scale) among the selected food portions. If the amount of carbohydrate alone were an adequate basis for glycemic response, a threefold increased response to lentils compared with Rice Bubbles and a twofold increased response to apple compared with white bread would be expected. Taking both the quantity and quality of carbohydrate into account, the majority of foods fell within a remarkably narrow range. Thus, consideration of both the GI and the carbohydrate content provided a better estimate of true postprandial glycemia.

The very low glucose response to lentils was particularly surprising considering that this portion contained by far the most carbohydrate (40 g compared with 15 g in one slice of white bread). Our finding might be explained by the presence of starch in legumes that is so slowly digested and absorbed that its effect on glycemia is minimal regardless of the load size. Digestion may be particularly prolonged when the portion size is large, slowing gastric emptying. The rate of glucose appearance in the bloodstream may then be low enough to match the rate of glucose disappearance by transport into cells. Although legumes also contain resistant starch, which is not digested and absorbed at all (22), our calculated portion sizes were based on available starch plus sugars, not total carbohydrate. Residual activity of enzyme inhibitors in legumes may directly interfere with digestion and absorption, and this in turn may affect the glucose response.

Our dose-response relationships showed that increasing the glycemic load in a stepwise fashion produced predictable stepwise increases in glucose AUC. This was especially true at the lower doses (equivalent to one, two and three slices of bread); however, at higher doses (equivalent to four and six slices of bread) there was a leveling off in glycemia. Thus, a twofold increase in glycemic load (dose) produced only an 50% increase in the AUC. Healthy individuals, unlike those with impairments in glucose tolerance, are able to control glycemia within narrow physiological boundaries by increasing the amount of insulin secreted (23). This capacity varies somewhat from day to day under the influence of a variety of factors including previous food intake and exercise patterns. Food Set A, in which all the tests were repeated, produced a high day-to-day variation in the glycemic effect of the same dose of the same food (intraindividual CV = 54%). This is higher than the 23% seen within the standard GI testing protocol (20). This was in part because the smallest doses were associated with a small AUC, in which the marginal differences translated into a comparatively large CV (e.g., 20 compared with 40 AUC units translates to 100% variation, but 110 compared with 120 translates to <10% variation).

Our findings also suggest that glycemic load provides a reasonable estimate of insulin demand. Study 2 demonstrated that progressive increases in glycemic load from the equivalent of one to six slices of bread, regardless of food source, produced insulin responses that were directly proportional to the load. Hence, doubling or tripling the load doubled or tripled the insulin AUC with no sign of a threshold as was the case for glycemia. This indicates that diets with a high glycemic load have a true physiological basis for predicting the risk of type 2 diabetes. Excessive insulin demand has been hypothesized to lead to ß-cell exhaustion in susceptible individuals (23). However, hyperglycemia resulting from glucose toxicity may also impair ß-cell function.

Profound differences were observed in postprandial insulinemia among individuals, despite the fact that glycemic responses were similar (Fig. 3). Set A (i.e., one group of 10 subjects) showed much greater insulin AUC at each dose level than Set B (another group of 10 subjects, P < 0.001), implying greater insulin resistance in the first group. We have previously reported significant differences in postprandial insulinemia in lean young adults of different ethnicities (24). The differences are not simply due to the nature of the foods in Set A compared with Set B; white bread, which was common to both food sets, also produced marked differences.

In addition to validation of the physiological basis of glycemic load in epidemiological studies, our findings may have a clinical application. If glycemic load predicts insulin demand in healthy individuals, it is also possible that it is a more accurate predictor of exogenous insulin requirements for different meals in individuals with type 1 diabetes. Thus, rather than a carbohydrate:insulin ratio, a glycemic load:insulin ratio might be used to calculate insulin dose before meals. With the publication of the latest comprehensive GI tables (25), glycemic load values are also provided for nominal serving sizes of over 1000 different foods. Thus, researchers now have the tools to undertake clinical studies in this area.

Although these early findings are promising, the limitations of this study need to be considered. The number of subjects and foods was small. The CV was high, particularly at the lowest dose. The subjects were lean and relatively young and therefore not representative of the general population. The same relationships may not exist in subjects with varying degrees of insulin resistance, overweight or obesity. The insulin dose-response relationships are not applicable to those with severe type 2 diabetes in whom ß-cell function may be compromised. Not all foods followed the trend; lentils and basmati rice, both of which have a low GI, produced lower than predicted responses in Study 1. Studies of mixed meals and day-long glycemic and insulin profiles are needed to properly validate the concept of glycemic load.

For commentary on this article, see the article by Ludwig in this issue (26).

	FOOTNOTES

 
¹ Supported by the Diabetes Australia Research Trust.

³ Abbreviations used: AUC, area under the curve; CHO, carbohydrate; GI, glycemic index.

Manuscript received 8 April 2003. Initial review completed 16 May 2003. Revision accepted 10 June 2003.

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26. Ludwig, D. S. (2003) Glycemic load comes of age. J. Nutr. 133:2695-2696.[Free Full Text]

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