The Potential of an in Vitro Digestion Method for Predicting Glycemic Response of Foods and Meals
Abstract
:1. Introduction
2. Materials and Methods
2.1. Foods Subjected to in Vitro Digestion
2.2. Meals Subjected to in Vitro Digestion
2.3. Overview of Foods or Meals Subjected to in Vitro Digestion
2.4. In Vitro Digestion Protocol
2.5. Data Analysis
3. Results
3.1. Dialyzable Glucose Released during the in Vitro Digestion of Foods or Meals
3.2. Correlation of Dialyzable Glucose with GI
3.3. Correlation of Dialyzable Glucose with GL
3.4. Correlation of Dialyzable Glucose with Glycemic Response
4. Discussion
- (a)
- This in vitro digestion method used a dialysis membrane to separate the soluble low molecular weight fraction that reflects the absorbed fraction of glucose or other nutrients [27,29,31,32,33]. Dialysis bags or tubes have been previously employed in in vitro methods but in this protocol we propose the use of dialysis membrane fastened with an elastic band to a cylindrical insert in a six-well plate [34]. This approach draws from previous developments in in vitro digestion methodology and offers certain practical advantages [35]. In particular, the required amount of food sample is much smaller (2 mL homogenate food) compared to previous proposed methods. This reduced amount of food results in lower amounts and concentrations of reagents and enzymes required. Moreover, the option of stacking six-well plates in the incubator, instead of inserting vials in a space-limiting water bath, facilitates the simultaneous, simple and well-organized testing of many samples. Therefore, it reduces both the time and cost of the analysis and increases efficiency.
- (b)
- The index that reflects glycemic response is dialyzable glucose determined spectrophotometrically at 120 min after the second phase of the in vitro digestion. Various indices (carbohydrate digestion rate (rapid/slow), hydrolysis index, glucose equivalents) have been previously used for the correlation of in vitro carbohydrate digestion with glycemic response in humans or the GI of meals [13,15,28,36,37,38]. For example, Englyst et al. [13] found that the in vitro measurement of rapidly available glucose in foods can reflect the glycemic response employing a rapid yet more sophisticated set up than the proposed herein.
- (c)
- The chewing process has been simulated through the use of a homogenizer, followed by treatment with human salivary α-amylase. Simulation of the oral phase is clearly important when carbohydrate digestion is studied. Mechanical breakdown is preferential, as the use of human chewing as employed by other studies raises practical issues when used in routine testing such as inter-subject differences in chewing, enzyme activity, saliva volume as well as other variations between human. These variations limit the ability to achieve reproducible in vitro digestion results [15,19]. It must be mentioned, however, that mechanical food breakdown may damage the food matrix thus altering the physical form of the food, a factor that has been argued to affect the glycemic response [4,39,40].
- (d)
- The simultaneous prediction of glycemic response and of mineral bioavailability in one experimental set up may be achieved, a setup which has been already utilized in the prediction of zinc and iron bioavailability. To retain this advantage, the time of pepsin incubation was increased to 120 min as previously proposed [29] although in most in vitro carbohydrate digestion protocols this step lasts from 30 to 60 min [12]. It must be noted that other protocols were not initially designed for the prediction of glycemic response [16,17].
- (e)
- The incorporation at the intestinal phase of the digestion process of fat-emulsifying bile salts to aid fat digestion is a comparative advantage of the proposed protocol. Lipid-starch interactions can decrease starch susceptibility to digestion [12] and thus the composition of fat in food has been suggested to effect glycemic response [39].
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Food Item | Quantity (g) | Sugar (g) | Fiber (g) | Fat (g) | Protein (g) |
---|---|---|---|---|---|
Breakfast cereals | |||||
Chocolate toasted rice i | 0.290 | 0.102 | 0.006 | 0.007 | 0.015 |
Corn flakes ii | 0.300 | 0.024 | 0.009 | 0.003 | 0.021 |
Whole wheat flakes and rolled raisins and roasted sliced hazelnuts and almonds iii | 0.390 | 0.064 | 0.030 | 0.024 | 0.043 |
Cereal grains | |||||
Rice long-grain iv | 0.870 | 0.002 | 0.003 | 0.004 | 0.024 |
Spaghetti n.5 white v | 0.780 | 0.012 | 0.011 | 0.005 | 0.044 |
Spaghetti n.5 whole meal vi | 0.850 | 0.015 | 0.022 | 0.011 | 0.055 |
Fruit | |||||
Banana vii | 1.250 | nr * | 0.030 | 0.011 | 0.011 |
Breads | |||||
White bread vii | 0.510 | 0.033 | 0.013 | 0.025 | 0.038 |
Whole meal bread ix | 0.570 | 0.036 | 0.030 | 0.029 | 0.046 |
Food Item | Quantity | Available Carbohydrates (g) | Sugar (g) | Fiber (g) | Fat (g) | Protein (g) |
---|---|---|---|---|---|---|
Beverages | ||||||
Energy drink with orange flavor 1 | 2.5 mL | 0.350 | 0.350 | 0.000 | 0.000 | 0.000 |
Carbonated orange juice 2 | 2.5 mL | 0.318 | 0.318 | 0.000 | 0.000 | 0.000 |
Natural apple juice 3 | 2.5 mL | 0.268 | 0.258 | 0.030 | 0.000 | 0.003 |
Breakfast cereals | ||||||
Chocolate toasted rice 4 | 0.3 g | 0.255 | 0.105 | 0.006 | 0.008 | 0.015 |
Corn flakes 5 | 0.3 g | 0.252 | 0.024 | 0.009 | 0.003 | 0.021 |
Whole wheat flakes and rolled raisins and roasted sliced hazelnuts and almonds 6 | 0.3 g | 0.190 | 0.049 | 0.023 | 0.019 | 0.033 |
Cereal grains | ||||||
Rice long-grain 7 | 1.5 g | 0.430 | 0.008 | 0.015 | 0.020 | 0.110 |
Spaghetti n.5 white 8 | 1.5 g | 0.480 | 0.053 | 0.045 | 0.023 | 0.188 |
Spaghetti n.5 whole meal 9 | 1.5 g | 0.400 | 0.053 | 0.077 | 0.038 | 0.188 |
Fruit | ||||||
Apple 10 | 1.2 g | 0.160 | nr * | 0.032 | tr ** | tr ** |
Banana 11 | 1.2 g | 0.240 | nr * | 0.029 | 0.010 | 0.010 |
Breads | ||||||
White bread 12 | 0.3 g | 0.147 | 0.020 | 0.008 | 0.014 | 0.023 |
Whole meal bread 13 | 0.3 g | 0.132 | 0.019 | 0.016 | 0.015 | 0.024 |
Legumes | ||||||
Small lentils 14 | 1.5 g | 0.180 | nr * | 0.118 | 0.008 | 0.136 |
Dairy products | ||||||
Skim milk 15 | 2.5 g | 0.119 | nr * | nr * | 0.000 | 0.085 |
Infant formula | ||||||
Milk for infant 16 | 1.0 g | 0.580 | nr * | nr * | 0.280 | 0.090 |
Tested Foods | Fat (g) | Carbohydrates (g) | Sugar (g) | Fiber (g) | Protein (g) |
---|---|---|---|---|---|
White bread (24 g) 1 | 1.2 | 14.900 | 0.800 | 1.300 | 2.500 |
Cheese (20 g) 2 | 1.7 | 0.300 | nr * | 0.000 | 5.600 |
C-chocolate (30 g) 3 | 9.0 | 11.900 | 10.900 | 2.000 | 1.300 |
D-chocolate (30 g) 4 | 5.3 | 7.300 | 0.100 | 2.700 | 0.800 |
C-jelly strawberry (165 g) 3 | 0.0 | 27.100 | 26.700 | 0.300 | 2.700 |
D-jelly strawberry (165 g) 4 | 0.0 | 8.400 | 0.100 | 2.600 | 3.400 |
C-milk dessert (160 g) 3 | 7.3 | 32.300 | 25.900 | 0.000 | 5.300 |
D-milk dessert (160 g) 4 | 2.3 | 22.900 | 7.400 | 5.100 | 5.200 |
C-crème caramel (120 g) 3 | 4.6 | 24.400 | 24.400 | 0.200 | 3.800 |
D-crème caramel (120 g) 4 | 1.2 | 7.800 | 0.300 | 4.000 | 0.900 |
C-cake (55 g) 3 | 8.7 | 32.500 | 18.400 | 0.400 | 3.600 |
D-cake (55 g) 4 | 8.0 | 20.000 | 0.200 | 3.700 | 3.400 |
C-mille-feuille (90 g) 3 | 11.2 | 28.100 | 15.500 | 0.800 | 3.100 |
D-mille-feuille (90 g) 4 | 4.0 | 17.900 | 0.200 | 3.700 | 2.500 |
C-pastry cream (65 g) 3 | 3.2 | 15.700 | 12.700 | 0.100 | 1.700 |
D-pastry cream (65 g) 4 | 1.5 | 9.900 | 0.240 | 2.700 | 1.600 |
Dialyzable Glucose vs. GL | Dialyzable Glucose vs. GI | Dialyzable Glucose Ratios 1 vs. Blood Glucose Ratios 1 | ||||
---|---|---|---|---|---|---|
Time (min) | Spearman’s rho | p | Spearman’s rho | p | Spearman’s rho | p |
0 | 0.656 | 0.006 | 0.333 | 0.381 | 0.152 | 0.605 |
30 | 0.723 | 0.002 | 0.500 | 0.170 | 0.490 | 0.075 |
60 | 0.833 | <0.001 | 0.667 | 0.050 | 0.363 | 0.203 |
90 | 0.854 | <0.001 | 0.750 | 0.020 | 0.336 | 0.240 |
120 | 0.953 | <0.001 | 0.800 | 0.010 | 0.736 | 0.003 |
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Argyri, K.; Athanasatou, A.; Bouga, M.; Kapsokefalou, M. The Potential of an in Vitro Digestion Method for Predicting Glycemic Response of Foods and Meals. Nutrients 2016, 8, 209. https://doi.org/10.3390/nu8040209
Argyri K, Athanasatou A, Bouga M, Kapsokefalou M. The Potential of an in Vitro Digestion Method for Predicting Glycemic Response of Foods and Meals. Nutrients. 2016; 8(4):209. https://doi.org/10.3390/nu8040209
Chicago/Turabian StyleArgyri, Konstantina, Adelais Athanasatou, Maria Bouga, and Maria Kapsokefalou. 2016. "The Potential of an in Vitro Digestion Method for Predicting Glycemic Response of Foods and Meals" Nutrients 8, no. 4: 209. https://doi.org/10.3390/nu8040209
APA StyleArgyri, K., Athanasatou, A., Bouga, M., & Kapsokefalou, M. (2016). The Potential of an in Vitro Digestion Method for Predicting Glycemic Response of Foods and Meals. Nutrients, 8(4), 209. https://doi.org/10.3390/nu8040209