Glycaemic Indices of Rye, Wholemeal Bread, and Glucose Consumption
Assessing the Relationship Between Nutritional Value and Postprandial Blood Glucose
by Timothy Girgis (2614615G)
BIOL 3A Human Biology
Dr. Katherine Price
University of Glasgow Glasgow, Scotland
October 12, 2022
1. Introduction
Nutritional labels have been shown to motivate healthier consumer choices (Shangguan et al , 2019), raising the question of whether additional metrics should be added to promote even healthier selections. One such metric is the Glycaemic Index (GI), which measures the potential of carbohydrates to elevate blood glucose over time Adding GI to labels could thus motivate consumers to choose options that would lessen the number of acute blood glucose spikes believed to result in the insulin resistance responsible for type II diabetes (Cleveland Clinic, 2021), among other risks. Alternatively, displaying GI could prove counterproductive if the GI of a less nutritious carbohydrate, such as refined (white) bread, does not differ significantly from healthier alternatives like rye or wholemeal bread, which would still provide greater nutrients (Raman and Kubala, 2019)
GI may be more likely to mislead consumers when displayed with the classifications of 'high' (GI ≥70),' medium' (GI = 55-69), and 'low' (GI <55) established by the International Organization for Standardization (2006), as these may obscure differences within categories.
On the other hand, numerical GI values should be less misleading, as foods with more intact microstructures and those packed with more nutrients should theoretically take longer to break down, reducing the rate of digestion and thus flattening the postprandial blood glucose curve.
Based on this theory, it was hypothesised that more nutritious carbohydrates will, ipso facto, always have GI values that are significantly lower (defined here as ≥5% lower and roughly proportional to nutritional values where such a clear-cut difference in values generally exists). To test this, blood glucose levels were measured after consumption of either rye or wholemeal bread and compared with a liquid glucose drink for reference, with the expectation that numerical GI would be lower for rye than wholemeal bread, which would, in turn, be lower than the default GI of the reference.
2. Methodology
2.1
Subjects and Grouping
Forty-five University of Glasgow students with no prior history of diabetes participated in the study from 2016-2022. Ten were assigned to the wholemeal bread group, twenty to the rye group, and fifteen to the glucose reference group.
2.2
Reference Food
Although the difference in GI between rye, wholemeal, and white bread is of great interest, a liquid glucose drink, rather than white bread, was used here as a reference, as ‘white bread’ includes various types of bread such as toast, sourdough, and pitta. Each has different ingredients, starch structures, and digestibility - resulting in different GI values (Miller and Stanner, 2016). Thus, liquid glucose was a better referent to identify a potential relationship between nutrient contents and GI.
2.3
Food Amounts
All three groups received precisely 50g of carbohydrates, with the necessary amount of each food calculated based on the below equation:
Amount (g)=
50g carbohydrate × serving size (g) carbohydrate content (g) fibre content (g) (1)
For the reference group, 625mL of the liquid glucose drink was required, while the rye group required 156.2g of rye bread based on 48.1g carbohydrates, 9.7g of which were indigestible fibre, per 100g serving. The wholemeal group required 161.0g wholemeal bread slices based on 37.8g carbohydrates, 6.8g of which were indigestible fibre, per 100g serving Bread portions were weighed for accuracy. The rye and wholemeal groups were allowed water and an optional pure butter spread. All subjects were required to fast ≥10 hours before the initial blood glucose reading.
2.4 Blood Glucose Readings
All blood glucose levels were obtained from whole capillary blood drawn and read via Abbott Optium Xceed® glucometers. An initial preprandial reading was obtained before subjects consumed their assigned food. Four postprandial readings were then taken at 30, 60, 90, and 120 minutes from when each subject finished their meal.
2.5 Calculations of GI
As GI measures the incremental area under the blood glucose elevation curve (AUC) over baseline versus time, mean blood glucose readings were used to calculate concentration (C) per 30-minute interval for each group. The baseline AUC was calculated using the mean initial preprandial concentration (C0) as given by the equation below. AUCbaseline (mmol/L) = C0 × 120 (2)
Where C0 corresponded to the height of the rectangle under the curve, and 120 was the length Four postprandial interval AUC values were then calculated using the equation below.
Where A and B refer to each time interval (i.e., 30-0min, 60-30 min, etc.). Total AUC values were then calculated by summating the four AUC values and subtracted from baseline AUC to obtain incremental AUC (iAUC). GI values were then calculated based on the relation of each test food iAUC to the reference, as given by the equation below.
(mmol/L × min) = �iAUCtest food iAUCglucose ⁄ � × 100 (4)
3. Results
3.1
Blood Glucose Concentrations
Mean preprandial (0 min) blood glucose concentrations were similar between all groups, in contrast to postprandial levels per 30-minute interval, as shown in Table 1 below.
Table 1. Mean and standard error (SE) blood glucose concentrations (mmol/L)
(Reference)
Bread
*Preprandial blood glucose; all subsequent readings are postprandial (i.e., 30 min after consumption).
Mean blood glucose concentrations for each group differed significantly at peak values, which all occurred around 30 minutes postprandially, as shown in Figure 1
Mean (± SE) Blood Glucose Concentrations Before and After 50g Carbohydrates from Liquid Glucose, Wholemeal Bread, or Rye Bread.
Blood Glucose Concentration (mmol/L) Time (minutes)
Wholegrain Bread Rye Bread
Figure 1 Mean (± SE) Blood Glucose Concentrations over 120 minutes. Liquid glucose reference (red line) has a significantly higher peak and thus “spiked” blood glucose levels much higher than either wholegrain (yellow line) or rye bread (green line). Similarly, wholemeal bread had a slightly higher peak than rye bread.
3.2 iAUC and GI Values
The iAUC and corresponding GI values also differed between groups, but the ISO categories resulted in no difference between rye and wholemeal bread, as they were both ‘medium,’ in contrast to the glucose reference, which was ‘high,’ as shown in Table 2 below.
Table 2. iAUC and GI values compared with ISO classifications.
4. Discussion
4.1 Analysis
These findings support the hypothesis that carbohydrates with greater nutrient content generally have lower numerical GI values. These results were also relatively in line with the findings of Lužnik et al. (2019) Crucially, using the ISO GI categories resulted in no difference between rye and wholemeal bread, as both fell within the ‘medium GI’ category.
Yet it is noteworthy that the numerical differences between the GI of rye bread and that of wholemeal (67 vs 64 mmol/L × min) were borderline in meeting the definition of ‘significant’ as determined for this analysis (the difference between the two values being 4.69%, which only meets the definition if rounded up). It is possible this definition was too stringent, as both rye and wholemeal bread have excellent nutritional value, and indeed both had significantly lower GI values than the reference liquid glucose drink, but it is also likely that the minimal difference between the two test foods related to a key limitation of this study.
4.2
Limitations
The buttery spreads allowed for the rye and wholemeal groups may have partially confounded these results As Collier, McLean and O’Dea reported, “[c]o-ingestion of fat resulted in a significant flattening of the post-prandial glucose curves” (1984, p. 50). Similarly, Gentilcore et al. observed lower peaks in addition to a “marked delay in the onset of the postprandial rise as well as the rate of increase in blood glucose” (2006, p. 2066) after the consumption of fats. Curiously, neither postprandial elevation nor time to peak was delayed
for any group here Peak concentrations were indeed lower for rye and wholemeal bread than for liquid glucose, but all occurred about the same time, around 30 minutes This would seem to support lower concentrations being due to the reduced glucose-elevating potential of the test foods. Yet SE values for means at 30 minutes were nearly twice as high for the wholemeal group as for the rye group (0.47 vs 0.24 mmol/L). This difference in variability does suggest an external factor impacted overall concentrations, as the sample here narrowly consisted of students from a single university, limiting the amount of variability from individual differences, particularly from accrued insulin resistance.
The greater variability within the wholemeal group likely relates to greater fat consumption, as the wholemeal test food was pre-sliced into thin pieces. This may have encouraged participants to butter more/all of their numerous slices versus the buttering of only a few thickly hand-cut rye pieces. Interestingly, a similar SE value can also be observed for the glucose reference group (0.44 mmol/L). This may be due to the change from Lucozade® to a more generic uncarbonated liquid glucose drink after the reformulation of Lucozade® in 2017.
Regardless, these SE values are only significant compared to the rye group, but they are not so substantial as to cast doubt on the reliability of the underlying trend.
4.3 Conclusion and Future Research
Given that the GI value for rye was still lower than that of wholemeal despite the group associated with the latter having likely consumed more fat and that both GI values were significantly lower than that of the liquid glucose referent, it is likely that the trend observed here is accurate. It would thus appear that there is little risk of consumer misunderstanding if GI numerical values are added to nutrition labels. Future studies should attempt to use a crossover design to wholly exclude individual variability and perhaps quantify the precise impact of fat on carbohydrate digestion – though this may differ too significantly between individuals to be useful.
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