Abstract
BACKGROUND:
Carbohydrates as starch are a staple part of human diet. Upon starch digestion, glucose is absorbed, eliciting an insulin response. Glucose absorption kinetics (rapid or slow) depend on starch structure. Products made from wholemeal/wholegrain flour cause moderate glycemic and insulinemic responses and support a healthy lifestyle.
OBJECTIVE:
To review the nutritional value in terms of the in vivo glycemic and insulinemic index and the in vitro digestibility characteristics of six wholemeal/wholegrain commercial bakery products.
METHODS:
We analyzed in vitro the rapidly- and slowly- available glucose (RAG and SAG), the rapidly- and slowly- digestible starch (RDS and SDS), and the resistant starch (RS) fraction of the six wholemeal/wholegrain products against one white type of bread. The glycemic (GI) and the insulinemic index (II) were estimated in vivo in a group of eleven healthy individuals.
RESULTS:
The glycemic indices of the wholemeal/wholegrain flour biscuits and breads were low, (range 28 ± 3.2 to 41 ± 3.9, Mean+SEM) correlating with the insulinemic indices. RAG positively correlated with both GI and II, with fiber having a marginal correlation.
CONCLUSIONS:
Our findings indicate that both conventional and non-conventional wholemeal/wholegrain bakery products have low GI and moderate II, correlating to in vitro starch digestibility and type of processing.
Introduction
Carbohydrates are a common constituent of the human diet, representing 50% of daily energy intake in the Western diet [1]. In a typical diet, carbohydrates are mostly available as starch. Starch is digested in the human small intestine in order to deliver glucose that is absorbed into the blood. The rate and the extend of the digestion of the carbohydrate consumed (rapidly-, slowly digestible or resistant starch) is influenced by its botanical origin as this determines the amylose:amylopectin ratio and the structure of the starch granule [2]. Therefore, rapidly available glucose (RAG) and slowly available glucose (SAG), reflect the rate at which glucose (from sugars and starch, including maltodextrins) becomes available for absorption in the human small intestine and appears in the blood [3]. Finally, the macronutrient content (fat, fiber, protein etc.) of the diet strongly influences the rate of digestion and the subsequent physiological responses to glucose absorption manifested as glycemia and insulinemia [4]. Food processing is also important, as this determines the extend of starch gelatinization, particle size and the integrity of plant cell wall [5].
The glycemic index (GI) has been introduced in order to classify carbohydrate foods based on how they influence postprandial plasma glucose response. GI is expressed as the percentage increase in the glucose area under the curve of a test food against a standard food such as glucose or white bread [6]. Apart from the food composition, the GI is also influenced by the interplay of post-absorptive metabolic events wherein insulin has the most important contribution. Therefore, the GI is closely related to the insulinemic index (II) as insulin is secreted in the blood in direct response to the postprandial increase in blood glucose and coordinates both glucose metabolic handling as well as clearance of any excess from the circulation. Both GI and II can be determined in tandem for a given test food with concomitant measuring of insulin as well as glucose levels in the same blood sample. Many types of food have been classified in terms of their glycemic index.
Wheat-based foods made from processed white flour mixes contain high concentrations of readily digestible starch. This type of starch can cause a high postprandial glycemic load and put a challenging pressure upon the physiological processes of glucose homeostasis by eliciting a dramatic and prolonged insulin response [7]. Chronic exposure to such a condition (in combination with the host lifestyle and sedentary habits), encourages obesity and increases the relative risk for type 2 diabetes mellitus, particularly correlating the combination of high glycemic load with low cereal fiber intake [8, 9].
On the other hand, diets of low glycemic index by being rich in slowly digested carbohydrates and high in fiber, offer considerable protection against metabolic disease [10] and can be an effective intervention for diabetes management [11].
To this end, replacing staples such as cereal-based bakery products made from processed with those made from non-processed wholemeal/wholegrain wheat flour, can effectively lower the glycemic response and positively contribute to a healthier diet [12].
Bakery goods (biscuits and bread) are popular complements of a typical Mediterranean diet. Because there is high demand of staples that could combine high energy and nutrient merit with low glycemic load, those bakery goods that are made of non-conventional, non-processed flour mixes and are enriched with whole grain cereals and plant protein, may provide a healthier alternative to those made with processed flour mixes [13, 14].
The aim of the present study was to review the nutritional merit in terms of the postprandial glycemic and insulinemic effect of particular commercial biscuits made from non-processed wholemeal and wholegrain flour mixes (consisting of whole grain spelt, whole grain oat, barley and lupine flour as well as other ingredients (tahini (sesame mash), figs) that have been utilized in the traditional Mediterranean and particularly the Greek diet since antiquity [15]. Spelt wheat (Triticum spelta) in particular has been cultivated in the Mediterranean since 5000BC [16]. Three types of wholemeal/wholegrain bread containing 10% plant protein, sunflower kernels, linseed, pea fiber, sesame seeds, salt, wheat bran, malt flour (barley, wheat), malt extract (barley) and sourdough were also studied. White bread was used as reference. The biscuits and bread were tested using an in vitro digestion assay by a modification of the method of Englyst et al [17] so as to determine their SDS, RDS, RS, RAG and SAG. Furthermore, we estimated the corresponding glycemic and insulinemic indices in a group of healthy volunteers and correlated the particular composition (wholemeal/wholegrain vs processed flour) and the corresponding type of carbohydrate defining glucose mobilization kinetics (RAG vs SAG) with the postprandial glycemic and insulinemic response.
Materials, subjects and methods
Test bakery products
Three commercially available biscuits and three types of bread were used in the present study. Their composition is shown in Table 1. All products tested, contained wholemeal/wholegrain flour mixes and were compared to a reference white bread (Bread 3) that was made from processed wheat flour. The breads also contained whole kernels/seeds and sourdough. In more detail, Biscuit 1 (“Breakfast Belvita” also containing cinnamon and brown sugar was from NABISCO Mondelez Global LLC, East Hanover HJ 07936, USA). Biscuit 2 (“Olympic Runner” also containing cinnamon and tahini (sesame mash) and Biscuit 3 (“Athenian Philosopher” also containing fig and anise) were both made by OLYRA S.A., Alexandroupolis, Evros 68133 Greece P.O.B.:1095, https://olyrafoods.com/). All three wholemeal and the white-type bread flour mixtures and the breads were prepared and baked at the premises of OLYRA FOODS from flour mixes made by FLOURMILLS THRAKIS –I. OUZOUNOPOULOS S.A., Alexandroupolis, Evros, Greece.
Nutritional composition of the test bakery products
Nutritional composition of the test bakery products
(* contains components from wheat, rye and barley.)
Pepsin (from porcine gastric mucosa) 0.7 FIP-U/mg (catalog number: 1.07185.0100, Lot: K50525185 903) and pancreatin (from porcine pancreas) 350FIP-U/g protease, 6,000 FIP-U/g lipase, 7,500FIP-U/g amylase (catalog number: 1.07130.1000, Lot: F2049330 912) were from Merck KGaA, Darmstadt, Germany. Amyloglucosidase (from Rhizopus sp.) 5,000U; 42U/mg (catalog number: E-AMPGU, Lot: 171102) and Invertase, 300U/mg (catalog number: E-INVPD-SG, Lot: 100201 C) were from Megazyme, BRAY BUSINESS PARK, Southern Cross Rd, Bray, Co. Wicklow, A98 YV2, Ireland. Potato starch (catalog number: 102954, Lot: Q6572) was from MP Biochemicals LLC 29525 Fountain Pkwy, Solon, OH44139/Parc d; Innovation BP, 50067 Illkirch, France. Guar gum (G4129-250 G, Lot:SLBZ5048) was from Sigma, Georg-Heyken-Straße 14, 21147 Hamburg, Germany. Arabinose (catalog number: 10323-20-3, Lot:AS442591) was from Apollo Scientific, Whitefield Rd., Bradbury, Stockport, SK62QR, UK.
In vitro analysis
Samples of bakery products (containing <0.6 g carbohydrate) were weighted to the nearest milligram into 50-ml polypropylene centrifuge tubes. Free sugar glucose and fructose, RAG, SAG, total glucose and starch fractions (RDS, SDS and RS contents) were analyzed by the protocol of Englyst et al. [17]. Free sugar glucose and fructose, as well as glucose portions taken at 20-, 120- and 240- min endpoints (G20, G120 and G240 respectively), were collected in absolute ethanol and quantified by HPLC in a private laboratory (Q & Q Analysis, Food quality services laboratory, Mikrasiaton 67 & Kountouriotou, 38333, Volos, Greece) by standard validated analytical protocols utilized therein.
Subjects
Eleven healthy volunteers, (6 men and 5 women aged 40 + 20 years with body mass index (BMI) of 22.9 + 1.9 took part in the study. None of them had any history of metabolic disease, and they were not taking any drugs that might have influenced their metabolic profile. Ethical permission for the study (Code name/acronym: laquoSITOraquo and MIS code number: 5030522) was obtained from the Democritus University of Thrace Ethical and Deontology Committee (DUTh/CETHDE) by the decision number 4/10/25-06-2019, as stated in the memorandum by the registry number of DUTh/CETHDE/59714/519/05-07-2019).
In vivo measurements
On test days, after an overnight fast following a day on which they were advised to abstain from alcohol and excessive exercise, the participants arrived at the laboratory and an iv cannula was inserted into the antecubital vein. A fasting blood sample was taken before the test bakery product was provided. The product was eaten within 10 minutes and subsequent blood samples were taken at 15, 30, 60, 90 and 120 minutes after consumption, into vacutainers without anticoagulants and were allowed to clot, immediately centrifuged, and kept at –80°C till analysis. All volunteers were tested in sequence consuming one product at a time. The order was randomized and different for each subject with only their personal understanding as to its identity (bread or biscuit), with intervals of >1 week between each test. Portion sizes were 60 g for the four types of bread. For the biscuits, 50 g of Biscuit 1 and 37,5 g of Biscuits 2 and 3 were administered as individually packed laquosuggested portionraquo by the respective manufacturers. The volunteers also received orally 30 g of soluble pure glucose (Merck) as a reference experiment in order to assess the area under the curve (AUC) under optimally available laquofreeraquo glucose. Blood insulin was measured by RIA (I-125 IRMA Kit REF: RK-400CT, by IZOTOP, Institute of Isotopes Ltd, H-1535 Budapest, P.O.B.851 Hungary). Glucose was measured in a SIEMENS ADVIA automatic analyzer using a Medicon Kit (REF: 1417-0017, MEDICON HELLAS AE, Greece).
Calculations and statistics
RAG, SAG and the various starch fractions were calculated by the formulae detailed by Englyst et al [17] as follows: RAG = G20 SAG = G120 - G20 RDS=(G20 - FSG) x 0.9 SDS=(G120 –G20) x 0.9 Total starch=(total glucose -FSG) x 0.9 Resistant starch=(total glucose –G120) x 0.9
The glycemic and the insulinemic responses were calculated as the incremental areas under the respective curves above the fasting levels, ignoring any area below the fasting value.
All statistical analyses were performed using the R programming language version 4.1.2 [18]. Linear regression was performed using the base R lm command. p values <0.05 were considered statistically significant. Graphics were drawn using the ggplot2 package (version 3.3.5).
Results
In vitro analysis
The nutrient profile (g/portion used in the glycemic index test) of the seven selected bakery products is shown in Table 1. The content of the in vitro digestion fractions (g/100 g of product), GI and II data for each product are shown in Table 2. Mean RAG content was 9.48 ± 1.41 g per 100 g product for the three wholemeal/wholegrain biscuits and bread; the white bread had a RAG value of 45.5 g per 100 g of product. Mean SAG was 8.48 ± 2.41 g per 100 g product for the three wholemeal/wholegrain biscuits and bread; the white bread had a SAG value of 0.8 g per 100 g of product.
Glycemic and insulinemic indices and starch digestibility parameters (g/100 g of product) of the test bakery products
Glycemic and insulinemic indices and starch digestibility parameters (g/100 g of product) of the test bakery products
G20, G120 and G240 are the amounts of glucose released at the timepoints of 20, 120 and 240 min respectively, as estimated by the in vitro enzymatic digestion assay of Englyst et al. [17] with the modifications of Gourineni et al. [14]. FSG: free sugar glucose, FSF: free sugar fructose, RAG: rapidly available glucose, SAG: slowly available glucose, TS: total starch, RS: resistant starch, RDS: rapidly digestible starch, SDS: slowly digestible starch, SEM: Standard Error of the Mean.
In Figs. 1 2, the glycemic and insulinemic response for the seven test foods compared to the 30 grams of orally administered glucose are shown, respectively. In Table 2, the GI and II of the test foods are shown. The mean GI of the wholemeal flour biscuits and breads was 33.7 with a range from 28 to 41, with the white bread having a GI of 89 ± 9.1. The mean II of the wholemeal biscuits and bread was 21 with a range from 22 to 31, with the white bread having an II of 54 ± 4.9.
Increase in blood glucose (±SEM) after consumption of the 7 test foods. The glucose increase after oral administration of 30 gr glucose is shown in all subgraphs. For comparison purposes, all x and y axes ranges are the same.
Increase in serum insulin (±SEM) after consumption of the 7 test foods. The insulin increase after oral administration of 30 gr glucose is shown in all subgraphs. For comparison purposes, all x and y axes ranges are the same.
Various linear regression models were constructed to determine the dependence of the GI upon food RAG, protein and fiber content. For these models, one extreme outlier (with a glycemic response near 0) was excluded. The results are shown in Table 3.
Dependence of the Glycemic Index on RAG, fiber and protein content
Dependence of the Glycemic Index on RAG, fiber and protein content
GI: Glycemic Index, Intrcpt: Intercept, coef: Coefficient, SEM: Standard Error of the Mean, RAG: Rapidly Available Glucose.
The best model (as determined by the R2 value) was that for the dependence of the GI only upon RAG (model number 1). The R2 was 0.952, with F(1,5)=120.8, p = 0.0001. The intercept β0 was 19.2, with a standard error of 2.7 (p = 0.0008) and the slope β1 was 1.5 with a standard error of 0.14 (p = 0.0001).
The R2 for the dependence of the GI on either fiber or protein content (models 2 and 3) were low (0.288 and -0.191, respectively), with the respective fiber and protein coefficients not being statistically significantly different from 0.
When the linear dependence of the GI upon RAG and either fiber or protein was tested (models 4 and 5), the R2 values were good (0.941 and 0.945, respectively). However, neither the coefficient for fiber content (model 4), nor the one for protein content (model 5), were statistically significantly different from 0, whereas the RAG coefficient remained statistically significantly different from 0 and with almost the same values as in model 1, having RAG as the only independent variable (1.5 ± 0.14 for RAG only, 1.6 ± 0.21 for RAG plus fiber and 1.5 ± 0.15 for RAG plus protein).
When the RAG was not included as an independent variable and the GI dependence upon only fiber and protein was tested (model 6), the R2 value was moderately good (0.564), but much lower than the R2 for RAG only. Furthermore, the coefficient for fiber was of marginal statistical significance (p = 0.036), whereas the protein dependence was not statistically significant (p = 0.1111).
Finally, when all independent variables (RAG, fiber and protein) were included in the model (model 7), the R2 value was very good (0.930). However, only the RAG dependence remained statistically significant (p = 0.0183) and in the same range as in models 1, 4 and 5 (1.4 ± 0.30), whereas the fiber and protein coefficients were not statistically significant.
When, instead of RAG, SAG was used as an independent variable, in none of the models was there a statistically significant SAG coefficient. When both RAG and SAG were used, only the coefficient for RAG was statistically significant (data not shown).
For the linear regression models described below, two extreme outliers with insulinemic response ≅ 0 were excluded.
Dependence of the insulinemic index on RAG, fiber or protein content, dovetails with that of the glycemic index, as shown in Table 4. The best model was again the one with RAG (model 1) as the only independent variable (R2 = 0.922), with intercept = 18.9 ± 1.8 and a coefficient for RAG = 0.8 ± 0.09, with both being statistically significant different from 0.
Dependence of the Insulinemic Index on RAG, fiber and protein content
Dependence of the Insulinemic Index on RAG, fiber and protein content
II: Insulinemic Index, Intrcpt: Intercept, coef: Coefficient, SEM: Standard Error of the Mean, RAG: Rapidly Available Glucose.
When either fiber or protein were the sole independent variables (models 2 and 3), the R2 was not good when compared to model 1 (0.290 and -0.189, respectively). Also, both the intercepts and the linear coefficients were not statistically significant different from 0.
When the RAG and either fiber or protein were used as independent variables (models 4 and 5), the R2 was good (0.903 and 0.908, respectively), but neither the fiber coefficient in model 4, nor the protein coefficient in model 5, were statistically significantly different from 0. On the other hand, the linear coefficient for RAG in all three models was essentially the same (0.8 ± 0.09 for model 1, 0.8 ± 0.14 for model 4 and 0.8 ± 0.10 for model 5). In all three models, the RAG coefficients were statistically significant, although for model 4 and 5 the significance was reduced.
When fiber and protein were used together as independent variables (model 6), but in the absence or RAG, the R2 (although satisfactory, with a value of 0.582), was much less than that of model 1, and only the fiber coefficient was of marginal statistical significance.
Finally, when all RAG, fiber and protein were included in the model as independent variables (model 7), the R2 was good (0.889), but neither the fiber nor the protein coefficients were statistically significant. Only the linear coefficient for RAG remained marginally significant, and in the same range as in models 1, 4 and 5 (0.7 ± 0.19).
As with the glycemic index, when SAG was used as the independent variable instead of RAG, again no statistically significant SAG coefficient was evident. When both RAG and SAG were included, only the RAG coefficient was statistically significant (data not shown).
A linear regression model was created with the glycemic index as the independent parameter and the insulinemic index as the dependent one. The graph of the linear regression line is shown in Fig. 3.
Scatterplot and linear regression line for the dependence of the insulinemic upon the glycemic index.
A strong linear dependence of the insulinemic upon the glycemic index is evident. The R2 was 0.979, F(1,5)=275.5, p <0.00001. The intercept was 9.09 ± 1.40 (p = 0.0015) and the slope was 0.51 ± 0.03 (p <0.00001).
In the present study, we analyzed six bakery products (three commercially available biscuits and three types of bread) made from wheat mixed with non-conventional flour mixtures. The biscuits contained whole grain spelt, whole grain oat, barley and lupine flour and the breads contained pregelatinized wheat flour, sunflower kernels, linseed, pea fiber, sesame seeds, wheat bran, malt flour from barley and wheat, malt extract from barley as well as dried sourdough (Table 1). In an in vivo experiment, we compared these products against orally administered glucose and one white type of bread, as a reference example containing a conventionally processed flour mixture. We correlated the results from both in vitro and in vivo testing methodology utilizing several statistical models in order to study the influence of inherent food properties (glucose content, type of starch, the presence of other macronutrients -protein, fiber-) and different food processing upon both the Glycemic and Insulinemic index.
To this end, we analyzed the SDS, RDS and RS fractions of the test bakery products by an in vitro assay measuring the amount of glucose released at different time points during incubation with digestive enzymes under standardized conditions [17]. We also determined both the GI and II as in vivo reporters of the direct glycemic response to these foods upon consumption.
In line with previous reports, we found that the RAG content strongly affects both the GI and the II [19]. Although there were no significant differences in the RAG fraction among the three biscuits and the three types of wholemeal/wholegrain bread tested (9.48 ± 1.41 g of glucose /100 g of product), the RAG estimated for the white bread, with a value of 45.5 was by far the highest.
Similarly, both the GI and II for the bread made from white processed flour were much higher (89 ± 9.1 and 54 ± 4.9 respectively) compared to those for the wholemeal/wholegrain products.
As shown in Table 2 by reference to oral glucose, all wholemeal/wholegrain breads tested, had remarkably low GI (<41). Various authors classify breads including whole grain as having variable GIs with some types having a low GI and most types being of medium GI [20, 21]. The low GI observed for the types of wholemeal/wholegrain breads tested here, can possibly be attributed both to their fiber and whole grain content, as well as the type of sourdough used.
From the application of a variety of linear regression models that were tested for the glycemic (Table 3) and insulinemic (Table 4) indices, for all bakery products tested it can be concluded that the main factor affecting both the glycemic and the insulinemic index is the rapidly available glucose (RAG).
In the case of the glycemic index, the R2 was 0.952, which means that variation in RAG explains 95.2% of the GI variation. The linear coefficient for RAG was 1.5 ± 0.14, meaning that (under the conditions of this study), an increase of 1 unit in RAG, correlates with an increase of the glycemic index by 1.5 ± 0.14 units. The RAG was the only independent variable found to be correlated to the glycemic index in a statistically significant manner.
For both GI and II, when SAG was used in the place of RAG, no statistically significant correlation was observed. This was also the case when both RAG and SAG were included. This shows that the main determinant of both the GI and the II is the glucose that is rapidly available for absorption from the digestive tract (glucose available in the first 20 minutes in the in vitro digestion) and not the more slowly available glucose (glucose released between 20 and 120 min in the in vitro digestion).
The only other coefficient of marginal statistical significance was that for fiber, when used in a model with fiber and protein together as independent variables. In this case the coefficient for fiber was -14 ± 4.51, meaning that an increase of 1 unit in fiber content, correlates with a decrease of the glycemic index by 14 units. However, this model explained only 58.2% of the GI variation, as opposed to 95.2%, with RAG being the most prominent independent variable.
Similar correlations as in the case of the glycemic index were also observed in the case of the insulinemic index. Variation in RAG alone explained 92.2% of variation in the insulinemic index, with a 1 unit increase in RAG being correlated with an increase of 0.8 ± 0.09 units in the insulinemic index. When fiber and protein were included as independent variables in the absence of RAG, the model explained 58.2% of the insulinemic index variation, with a decrease of –7.2 ± 2.25 units for a 1 unit increase in fiber, but again being of marginal statistical significance.
For all products tested, when the linear dependence of the II upon the GI was examined (shown in Fig. 3), the resulting R2 was 0.979, meaning that the variation in GI explains 97.9% of the II variation. Also, the slope was 0.51 ± 0.03, meaning that for a 1 unit increase in the GI, the II increases by approximately 0.5 units.
Interestingly, both the observed glycemic and insulinemic indices were marginally dependent on fiber content when it was considered as an independent variable. Therefore, our findings support the current knowledge that mainly RAG and the presence of fiber certainly highlight a major difference in nutritional composition between wholemeal/wholegrain and processed white flour in the bakery products tested. Wholemeal flour still contains all the natural fiber available in the entire kernel of the grain, by contrast to white flour that contains mostly the endosperm portion of the grain kernel with germ and bran having been removed [5]. Furthermore, when whole grains are included, individual plant cell integrity is more than occasionally maintained, and even single intact cereal cells can act as efficient barriers against amylolytic enzyme access [22]. It must also be noted that all wholemeal/wholegrain breads tested also contain barley and sourdough, two ingredients that lower the GI; barley because it is rich in β-glycans that interfere with amylolytic activity [23] and sourdough because fermentation of flour significantly reduces the rate of starch digestion [21, 24]. As for the processed white bread flour, baking in high humidity aids gelatinization and alters dramatically the properties of starch, making it more readily available to enzymic digestion [25]. It is therefore fair to say that fiber may not per se strongly affect GI and II as an independent variable. Nevertheless, the consequences of its removal may indirectly reflect on both indices by augmenting the rate of starch digestion (that is essentially RAG).
It is therefore the particular physical state (in essence a structural parameter) that relates to the macronutrient composition of wholemeal/wholegrain flour mixtures and the sourdough content in the three wholemeal/wholegrain bread tested, that very possibly explain their low GI and II observed for all products tested [25].
All biscuits tested contained historically documented ingredients [15] traditionally characterizing the Greek and Mediterranean diet in general. The presence of spelt in the biscuits “Olympic Runner” and “Athenian Philosopher”, containing an easily digestible kind of starch could have meant a higher GI [26]; nevertheless, in these two biscuits spelt was used as a wholegrain constituent (Table 1). Consequently, all biscuits had low RAG fractions as well as low GI and II. Apart from their wholemeal/wholegrain composition this finding can further be explained in terms of the fact that in contrast to bread, biscuits are commonly baked in dry heat, a state that prevents starch gelatinization and renders it even less available to enzymic digestion than in the case of bread [5].
Conclusion
To the best of our knowledge, there are limited studies that correlate the in vitro digestibility profile of foods with both the glycemic and the insulinemic index. Taken together, GI and II can provide an important insight into the post-absorptive glycemic physiology. Our observations provide further evidence that the RAG content of bakery products is the most prominent independent variable that correlates with both the glycemic and insulinemic response and characterizes test foods with respect to their glycemic properties.
Finally, bakery products made from a mixture of both conventional and non-conventional wholemeal/wholegrain flour mixtures have a low GI and a moderate II. By being a staple part of the diet, they may present a healthier alternative to those made with processed flour by offering protection and a potential positive dietary intervention against metabolic disease.
List of abbreviations
Glycemic Index Insulinemic Index Rapidly Available Glucose Slowly Available Glucose Rapidly Digestible Starch Slowly Digestible Starch (SDS) Resistant Starch Free Sugar Glucose Standard Error of the Mean
Funding
The present study was financed and implemented within the framework of the Single RTDI State Aid Action “Research-Create-Innovate”, under the acronym “SITO” and code number: 82147 (MIS code number: 5030522)- Co-financed by Greece and the E.U. The authors -members of staff and scientific collaborators- acting within clinics and laboratories in the premises of the Democritus University of Thrace and the SMEs OLYRA S.A. as well as FLOURMILLS THRAKIS –I. OUZOUNOPOULOS S.A., were partners in the laquoSITOraquo project.
Conflict of interest
The authors have no conflict of interest to report.
Author contributions
Charalampos Papadopoulos: Conception; Performance of work, (in vitro/in vivo study); writing the article. Constantine Anagnostopoulos: Conception; performance of work; interpretation of data; writing the article. Athanasios Zisimopoulos: Conception; performance of work. Maria Panopoulou: Conception; performance of work. Dimitrios Papazoglou: Conception; performance of work; interpretation of data. Anastasia Grapsa: Performance of work, (in vivo study). Thaleia Tente: Performance of work, (in vitro/in vivo study). Ioannis Tentes: Conception; performance of work; interpretation of data; writing the article.