Effect of prior meal macronutrient composition on postprandial glycemic responses and glycemic index and glycemic load value determinations

Abstract

Background: The potential impact of prior meal composition on the postprandial glycemic response and glycemic index (GI) and glycemic load (GL) value determinations remains unclear.Objective: We determined the effect of meals that varied in macronutrient composition on the glycemic response and determination of GI and GL values of a subsequent standard test food.Design: Twenty healthy participants underwent 6 test sessions within 12 wk. The subjects received each of 3 isocaloric breakfast meals (i.e., high carbohydrate, high fat, or high protein) on separate days in a random order, which was followed by a standard set of challenges (i.e., white bread and a glucose drink) that were tested on separate days in a random order 4 h thereafter. Each challenge provided 50 g available carbohydrate. Arterialized venous blood was sampled throughout the 2-h postchallenge period. GI, GL, and insulin index (II) values were calculated with the use of the incremental area under the curve (AUCi) method, and serum lipids were determined with the use of standard assays.Results: The consumption of the high-protein breakfast before the white-bread challenge attenuated the rise in the postprandial serum glucose response (P < 0.0001) and resulted in lower glucose AUCi (P < 0.0001), GI (P = 0.0096), and GL (P = 0.0101) values than did the high-carbohydrate and high-fat breakfasts. The high-protein breakfast resulted in a lower insulin AUCi (P = 0.0146) for white bread than did the high-fat breakfast and a lower II value (P = 0.0285) than did the high-carbohydrate breakfast. The 3 breakfasts resulted in similar serum lipid responses to the white-bread challenge.Conclusions: These data indicate that the macronutrient composition of the prior meal influences the glycemic response and the determination of GI and GL values for white bread. Future studies are needed to determine whether the background food macronutrient composition influences mean dietary GI and GL values that are calculated for eating patterns, which may alter the interpretation of the associations between these values and chronic disease risk. This trial was registered at clinicaltrials.gov as NCT01023646.

Keywords: glycemic index and glycemic load; glycemic response; healthy participants; macronutrient composition; nonesterified fatty acids.

© 2017 American Society for Nutrition.

Figures

FIGURE 1

Mean ± SD effects of different breakfasts on glycemic and insulin responses to the glucose-drink and white-bread challenges. Serum postprandial glucose (A) and insulin (B) concentrations from the glucose-drink and white-bread challenges that were preceded by breakfasts varying in macronutrient composition are presented. Differences in serum postprandial glucose or insulin concentrations in test breakfasts over a 2-h test period were determined with a 2-factor mixed ANOVA with the main effects of the test breakfast and time and test breakfast × time interaction with repeated measures for the participants after the glucose-drink and white-bread challenges, respectively. In the analysis of serum postprandial glucose concentrations, P values for the breakfast × time interaction after intakes of the glucose drink and white bread were P = 0.23 and P < 0.0001, respectively. In the analysis of serum postprandial insulin concentrations, P values of the breakfast × time interaction after intakes of the glucose drink and white bread were P = 0.34 and P = 0.0312, respectively. Because the breakfast × time interaction for white bread in both postprandial glucose and insulin analyses was significant at P ≤ 0.05, multiple comparisons at each time point were carried out with the use of the Tukey-Kramer method. Significance was accepted at P ≤ 0.05. Means with different letters are significantly different from each other at the same time point. n = 20. C-GLU, carbohydrate, glucose drink; C-WB, carbohydrate, white bread; F-GLU, fat, glucose drink; F-WB, fat, white bread; P-GLU, protein, glucose drink; P-WB, protein, white bread.

FIGURE 2

Mean ± SD effects of different breakfasts on glucose AUCi, glycemic index, and glycemic load values. Glucose AUCi values for the glucose drink (A) and white bread (B) and glycemic index (C) and glycemic load (D) values for white bread after consumption of breakfasts varying in macronutrient compositions are presented. Differences in glucose AUCi, glycemic index, and glycemic load values between test breakfasts over a 2-h test period were determined with the use of a mixed-design ANOVA model with the participant as a random effect and the test breakfast as a fixed effect. The Tukey-Kramer method was used for post hoc analyses. Significance was accepted at P ≤ 0.05. Means with different letters are significantly different from each other. n = 20. AUCi, incremental AUC; C-GLU, carbohydrate, glucose drink; C-WB, carbohydrate, white bread; F-GLU, fat, glucose drink; F-WB, fat, white bread; P-GLU, protein, glucose drink; P-WB, protein, white bread.

FIGURE 3

Mean ± SD effects of different breakfasts on insulin AUCi and insulin index values. Insulin AUCi values for the glucose drink (A) and white bread (B) and insulin index values (C) for white bread after consumption of breakfasts varying in macronutrient composition are presented. Differences in insulin AUCi and insulin index values between test breakfasts over a 2-h test period were determined with the use of mixed-design ANOVA model with the participant as a random effect and the test breakfast as a fixed effect. The Tukey-Kramer method was used for post hoc analyses. Significance was accepted at P ≤ 0.05. Means with different letters are significantly different from each other. n = 20. AUCi, incremental AUC; C-GLU, carbohydrate, glucose drink; C-WB, carbohydrate, white bread; F-GLU, fat, glucose drink; F-WB, fat, white bread; P-GLU, protein, glucose drink; P-WB, protein, white bread.

FIGURE 4

Mean ± SD effects of different breakfasts on postprandial serum NEFA, TAG, HDL-C, and LDL-C responses to the glucose-drink and white-bread challenges. Serum NEFA (A), TAG (B), HDL-C (C), and LDL-C (D) concentrations after glucose-drink and white-bread challenges preceded by breakfasts varying in macronutrient composition are presented. Differences in serum postprandial NEFA, TAG, HDL-cholesterol, and LDL-cholesterol concentrations between breakfasts over a 2-h test period were determined with the use of a 2-factor mixed ANOVA with the main effects of the test breakfast and time and test breakfast × time interaction with repeated measures for participants after intakes of the glucose drink and white bread, respectively. P values for the breakfast × time interaction after intake of the glucose drink were P = 0.0216, P = 0.91, P = 0.99, and P = 0.53 for NEFA, TAG, HDL-C, and LDL-C, respectively. P values for the breakfast × time interaction after white-bread intake were P = 0.78, P = 0.30, P = 0.40, and P = 0.31 for NEFA, TAG, HDL-C, and LDL-C, respectively. Because the breakfast × time interaction for NEFA after glucose-drink intake was significant at P ≤ 0.05, multiple comparisons at each time point were carried out via the Tukey-Kramer method. Significance was accepted at P ≤ 0.05. Means with different letters are significantly different from each other at the same time point. n = 20. C-GLU, carbohydrate, glucose drink; C-WB, carbohydrate, white bread; F-GLU, fat, glucose drink; F-WB, fat, white bread; HDL-C, HDL cholesterol; LDL-C, LDL cholesterol; NEFA, nonesterified fatty acid; P-GLU, protein, glucose drink; P-WB, protein, white bread; TAG, triacylglycerol.