Estimating the reliability of glycemic index values and potential sources of methodological and biological variability

Abstract

Background: The utility of glycemic index (GI) values for chronic disease risk management remains controversial. Although absolute GI value determinations for individual foods have been shown to vary significantly in individuals with diabetes, there is a dearth of data on the reliability of GI value determinations and potential sources of variability among healthy adults.

Objective: We examined the intra- and inter-individual variability in glycemic response to a single food challenge and methodologic and biological factors that potentially mediate this response.

Design: The GI value for white bread was determined by using standardized methodology in 63 volunteers free from chronic disease and recruited to differ by sex, age (18-85 y), and body mass index [BMI (in kg/m2): 20-35]. Volunteers randomly underwent 3 sets of food challenges involving glucose (reference) and white bread (test food), both providing 50 g available carbohydrates. Serum glucose and insulin were monitored for 5 h postingestion, and GI values were calculated by using different area under the curve (AUC) methods. Biochemical variables were measured by using standard assays and body composition by dual-energy X-ray absorptiometry.

Results: The mean ± SD GI value for white bread was 62 ± 15 when calculated by using the recommended method. Mean intra- and interindividual CVs were 20% and 25%, respectively. Increasing sample size, replication of reference and test foods, and length of blood sampling, as well as AUC calculation method, did not improve the CVs. Among the biological factors assessed, insulin index and glycated hemoglobin values explained 15% and 16% of the variability in mean GI value for white bread, respectively.

Conclusions: These data indicate that there is substantial variability in individual responses to GI value determinations, demonstrating that it is unlikely to be a good approach to guiding food choices. Additionally, even in healthy individuals, glycemic status significantly contributes to the variability in GI value estimates. This trial was registered at clinicaltrials.gov as NCT01023646.

Keywords: glycated hemoglobin; glycemic index; healthy volunteers; insulin index; variability.

© 2016 American Society for Nutrition.

Figures

FIGURE 1

Serum glucose and insulin response after consumption of the reference glucose drink and white bread. The symbol at each time point represents the mean ± SD for all subjects (n = 63).

FIGURE 2

Intra-individual and interindividual variability in glycemic index values for white bread. Each symbol represents the mean of 3 glycemic index value determinations in men [▪] (n = 33) and women [○] (n = 30). Horizontal bars depict the SDs.

FIGURE 3

Methodologic variables contributing to intra- and interindividual variability in glycemic index values for white bread including (A) 2 repeats of the reference and test food, (B) 3 repeats of the reference and test food, and (C) length of blood sampling period. For panels A and B, the bar at each time point represents the glycemic index mean ± SD for 10–63 subjects. For panel C, the bar at each time point represents the glycemic index mean ± SD for 63 subjects. The table below each figure shows the mean intra- and interindividual CV%, derived by using PROC VARCOMP. The square root of the subject variance divided by the mean glycemic index value yields the interindividual CV, whereas the square root of the error term divided by the mean glycemic index value yields the intra-individual CV.

FIGURE 4

Effect of method of AUC calculation on the intra- and interindividual variability in glycemic index values for white bread. The bar at each time point represents the glycemic index mean ± SD for all subjects (n = 63) calculated by using one of the following methods: AUCi, incremental AUC calculated geometrically as the sum of the areas of the triangles and trapezoids over 2 h, excluding the area below the initial fasting glucose concentration; AUCcut, cut AUC measured until the serum glucose concentrations first returned to the initial fasting glucose concentration; AUCmin, minimum AUC calculated by using the lowest serum glucose concentration as baseline; AUCnet, net AUC calculated by subtracting the sum of the negative areas of the triangles and trapezoids from the positive areas. The table below the figure shows the intra- and interindividual CV%, derived by using PROC VARCOMP. The square root of the subject variance divided by the mean glycemic index value yields the interindividual CV, whereas the square root of the error term divided by the mean glycemic index value yields the intra-individual CV.

FIGURE 5

Biological variables contributing to interindividual variability in glycemic index values for white bread (n = 63). PROC MIXED with a normal distribution for the error term was used to fit a random-intercept mixed-model to determine which biological factors contributed to the total variation in glycemic index at 120 min, assuming equal correlation among the 3 glycemic index values for each individual. HDL-C, high-density lipoprotein cholesterol; total C, total cholesterol.