ArticlePDF Available

Soy foods have low glycemic and insulin response indices in normal weight subjects

Authors:

Abstract and Figures

Foods with a low glycemic index (GI) may provide a variety of health benefits. The objective of the present study was to measure the GI and insulin index (II) of select soy foods. The study was conducted in two parts with low-carbohydrate products being tested separately. In Experiment 1, subjects averaged 23.2 years of age with BMI = 22.0 kg/m2, while subjects in Experiment 2 averaged 23.9 years of age with BMI = 21.6 kg/m2. The reference (glucose) and test foods were served in portions containing 10 g of carbohydrates in Experiment 1 (two test foods) and 25 g of carbohydrates in Experiment 2 (four test foods). Subjects consumed the reference food twice and each test food once. For each test, subjects were instructed to consume a fixed portion of the reference food or test food together with 250 g of water within 12 min. Blood samples were collected before each test and at 15, 30, 45, 60, 90, and 120 min after consumption of reference or test foods to quantify glucose and insulin. Two-hour blood glucose and plasma insulin curves were constructed and areas under the curves were calculated. GI and II values for each subject and test food were calculated. In Experiment 1, both low-carbohydrate soy foods were shown to have significantly (P < 0.05) lower GI and II values than the reference food. In Experiment 2, three of the four test foods had significantly (P < 0.05) lower GI and II values than the reference food. All but one of the soy foods tested had a low GI, suggesting that soy foods may be an appropriate part of diets intended to improve control of blood glucose and insulin levels.
Content may be subject to copyright.
BioMed Central
Page 1 of 10
(page number not for citation purposes)
Nutrition Journal
Open Access
Research
Soy foods have low glycemic and insulin response indices in normal
weight subjects
Robert M Blair*, EC Henley and Aaron Tabor
Address: Physicians Pharmaceuticals, Inc., 1031 E. Mountain St., Building 302, Kernersville, NC 27284, USA
Email: Robert M Blair* - drblair@revivalsoy.com; EC Henley - henleyec@aol.com; Aaron Tabor - drtabor@revivalsoy.com
* Corresponding author
Abstract
Background: Foods with a low glycemic index (GI) may provide a variety of health benefits. The
objective of the present study was to measure the GI and insulin index (II) of select soy foods.
Methods: The study was conducted in two parts with low-carbohydrate products being tested
separately. In Experiment 1, subjects averaged 23.2 years of age with BMI = 22.0 kg/m
2
, while
subjects in Experiment 2 averaged 23.9 years of age with BMI = 21.6 kg/m
2
. The reference (glucose)
and test foods were served in portions containing 10 g of carbohydrates in Experiment 1 (two test
foods) and 25 g of carbohydrates in Experiment 2 (four test foods). Subjects consumed the
reference food twice and each test food once. For each test, subjects were instructed to consume
a fixed portion of the reference food or test food together with 250 g of water within 12 min. Blood
samples were collected before each test and at 15, 30, 45, 60, 90, and 120 min after consumption
of reference or test foods to quantify glucose and insulin. Two-hour blood glucose and plasma
insulin curves were constructed and areas under the curves were calculated. GI and II values for
each subject and test food were calculated.
Results: In Experiment 1, both low-carbohydrate soy foods were shown to have significantly (P <
0.05) lower GI and II values than the reference food. In Experiment 2, three of the four test foods
had significantly (P < 0.05) lower GI and II values than the reference food.
Conclusion: All but one of the soy foods tested had a low GI, suggesting that soy foods may be
an appropriate part of diets intended to improve control of blood glucose and insulin levels.
Background
The glycemic index (GI) was first developed by Jenkins
and colleagues [1] as a new method of classifying foods
based on the blood glucose response after food consump-
tion. The GI value of a food is a percentage of the 2-hour
area under the blood glucose response curve of a reference
food, typically glucose [2]. Since the GI is determined for
a particular quantity of carbohydrates in the food being
tested and since the actual amount of carbohydrates con-
sumed in a meal or snack varies greatly, the GI concept
was expanded to include the concept of glycemic load
(GL). The GL is determined by multiplying the GI of a
food by the grams of carbohydrates in a serving. The GL
value incorporates the amount of digestible carbohydrates
in a serving in order to better gauge the impact of a meal
or snack on postprandial glucose response [3,4].
Published: 27 December 2006
Nutrition Journal 2006, 5:35 doi:10.1186/1475-2891-5-35
Received: 13 March 2006
Accepted: 27 December 2006
This article is available from: http://www.nutritionj.com/content/5/1/35
© 2006 Blair et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0
),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Nutrition Journal 2006, 5:35 http://www.nutritionj.com/content/5/1/35
Page 2 of 10
(page number not for citation purposes)
It has been reported that a high GI diet may have adverse
health consequences by increasing the risk for chronic dis-
ease [5,6]. Recent evidence suggests that high GI/GL diets
may increase the risk for cardiovascular disease (CVD) [7-
9] and type 2 diabetes [3,4,10,11]. A high GI diet may
increase the risk for chronic disease through the stimula-
tion of hyperglycemia and hyperinsulinemia [6].
In contrast, a low GI diet has been reported to have health
benefits [5,6,12,13]. A low GI diet has been shown to
improve glycemic control [14-17], aid in weight loss
[18,19], and reduce some CVD risk factors [9,14,20-23].
To date, only about 30 – 40 soy foods have been assessed
for their GI/GL values [1,24-27]. The objective of the cur-
rent study was to determine the GI and insulin index (II)
values of select soy food products (bars, drinks, pasta, and
chips) currently available on the U.S. and international
markets.
Methods
The current study was conducted using internationally
recognized GI methodology [28-30]. The experimental
procedures used in this study were in accordance with
international standards for conducting ethical research
with humans and were approved by the Human Research
Ethics Committee of Sydney University of Australia where
the study was conducted by contract.
A search of the literature using the National Library of
Medicine's PubMed search engine showed a paucity of lit-
erature on the determination of the glycemic index of soy
foods. A search of "soy AND glycemic index" yielded 5
hits; a search of "soy AND glycemic load" yielded 2 hits; a
search of "soy AND glycemic" yielded 14 hits and a search
of "isoflavone AND glycemic" yielded 5 hits. However,
none of the papers found measured the glycemic index or
glycemic load of soy foods. A search of the international
table of glycemic index and glycemic load values [26]
showed that the glycemic index of several soy foods has
been measured, but only a few of these have been
reported as independent publications.
Study Subjects
For both experiments, 10 healthy, non-smoking subjects,
were recruited from the staff and student population of
the University of Sydney. Exclusion criteria included being
overweight, dieting, impaired glucose tolerance, illness or
food allergy, or regular use of prescription medication
(other than contraceptive medication). All study partici-
pants gave written informed consent before participating
in this study.
In Experiment 1 the 10 subjects (two females, eight
males) had a mean age of 23.2 years (19.9–25.7 years)
and a mean body mass index (BMI) score of 22.0 kg/m
2
(absolute range = 19.4–25.4 kg/m
2
). In Experiment 2 the
10 subjects (four females, six males) had a mean age of
23.9 years (20.3–26.9 years) and a mean BMI score of
21.6 kg/m
2
(absolute range = 19.5–25.8 kg/m
2
). Four sub-
jects participated in both experiments.
Composition of Test Foods
In Experiment 1, the reference and two low-carbohydrate
test foods (products with 6 g net carbohydrates (availa-
ble carbohydrates); net carbohydrates = total carbohy-
drates – sugar alcohols – fiber) were served to subjects in
portions containing 10 g of digestible carbohydrate. In
Experiment 2, the reference and four test foods were
served to subjects in portions containing 25 g of available
carbohydrate. Glucose (Glucodin
®
powder, Boots Health
Care Company, North Ryde, NSW Australia) dissolved in
water was the reference food. Mass and nutrient contents
of the reference and test foods (Revival Soy
®
from Physi-
cians Pharmaceuticals, Inc., Kernersville, NC, USA) are
listed in Tables 1 and 2.
Each portion of the reference food was prepared the day
before required by dissolving the glucose in 250 g of water
and storing overnight at 4°C. The individual portions of
the test foods were prepared the day before required,
except for the soy spaghetti. The individual portions of the
uncooked soy spaghetti were weighed the day before
required. On the testing day, each portion of dry spaghetti
was cooked for 4 minutes in boiling water and drained.
The reference and test foods were served with 250 g of
plain water. The subjects consumed all the food and fluid
served to them at a comfortable pace within 12 minutes.
Experimental Procedures
The experimental methods used in the current study have
been previously described [31] and are briefly outlined
here. In both experiments, study subjects consumed the
reference food on two separate occasions and each of the
test foods on one occasion only after a 10-hour overnight
fast. The reference food was consumed on the first and last
test sessions, and test foods were consumed in random
order in between. Each test session was completed on a
separate morning with at least a day between subsequent
sessions.
On each test day a baseline, finger-prick blood sample was
obtained for blood glucose and plasma insulin determi-
nations using an automatic, non-reusable lancet device
(Safe-T-Pro
®
, Boehringer Mannheim GmbH, Mannheim,
Germany). Following consumption of the reference or test
food, additional blood samples were collected at 15, 30,
45, 60, 90 and 120 minutes. Blood glucose concentrations
were measured immediately after the blood samples were
collected. Blood samples collected for plasma insulin
Nutrition Journal 2006, 5:35 http://www.nutritionj.com/content/5/1/35
Page 3 of 10
(page number not for citation purposes)
determination were centrifuged for 30 seconds immedi-
ately after collection and the plasma layer from each sam-
ple was transferred into a labeled, uncoated
microcentrifuge tube and stored at -20°C until analyzed.
Blood Glucose and Glycemic Index Determinations
The glucose concentration in each of the whole capillary
blood samples was analyzed in duplicate using a glucose
dehydrogenase/mutarotase enzymatic reaction using a
HemoCue
®
beta-glucose photometric analyzer (HemoCue
AB, Ängelholm, Sweden). Duplicate readings were
accepted if the two separate measurements for each time
point were within 0.3 mmol/L of each other. If the read-
ings were not within 0.3 mmol/L of each other, then an
additional 2 blood glucose sample readings were taken
from the subject within approximately 40 – 60 seconds
after the initial readings. The two or three similar (i.e.
within 0.3 mmol/L) readings were then averaged together
to obtain the blood glucose response for that time point.
A two-hour blood glucose response curve was constructed
and the incremental area under the glucose response curve
(IAUC) was calculated. The GI value for each test food was
calculated for each subject by dividing the two-hour
blood glucose IAUC value for the test foods by their aver-
age two-hour blood glucose IAUC value for the reference
food and multiplying by 100 to obtain a percentage score.
The final reported GI value for each test food is the mean
GI value for that food in the group of 10 subjects.
Plasma Insulin and Insulin Index Determinations
The concentration of insulin in each plasma sample was
analyzed using a solid-phase antibody-coated tube radio-
immunoassay kit (Diagnostic Products Corporation, Los
Angeles, CA, USA). A two-hour plasma insulin response
curve was constructed from the data and the IAUC of the
insulin response curve was calculated. The II value for
each test food was calculated for each subject by dividing
their plasma insulin IAUC value for the test foods by their
mean plasma insulin IAUC value for the reference food
and multiplying by 100 to obtain a percentage score.
Table 2: Mass and nutrient contents of the test portions of the glucose reference and the four test foods in Experiment 2.
Test Food Portion Size (g) Energy (kJ
[Cal]) {kj/100 g
of product}
Protein (g)* {g/
100 g of
product}
Fat (g) {g/100 g
of product}
Available
Carbohydrate
(g) {g/100 g of
product}
Sugars (g) {g/
100 g of
product}
Fiber (g) {g/100
g of product}
Glucose
(Reference Food)
25.0 g glucose 250
g water
400 [95.5] 0.0 0.0 25.0 25.0 0.0
Peanut Butter
Chocolate Pal™
bar
48.4 813 [194.2] {1,671} 12.9 {26.7} 4.8 {10.0} 25.0 {51.7} 14.5 {30.0} 0.8 {1.7}
Chocolate
Daydream™ shake
– fructose
47.1 g powder
305.0 g water
742 [177.2] {1,567} 14.7 {31.3} 1.8 {3.9} 25.0 {53.0} 23.6 {50.0} 1.5 {3.1}
Lightly Salted
Sunshine™ soy
protein chips
48.1 808 [193.0] {1,672} 13.5 {28.0} 3.8 {8.0} 25.0 {52.0} 0.0 {0.0} 0.0 {0.0}
Soy spaghetti 42.4 (dry) 636 [151.9] {1,467} 10.6 {24.6} 1.1 {2.6} 25.0 {57.9} 1.5 {3.5} 0.8 {1.8}
1
The amount of water listed for some products is the amount of water needed to prepare that product. This volume is in addition to the 250 g of
water consumed with every product as part of the test methodology.
2
The protein in all of the products except the spaghetti is from soy. For the spaghetti, one serving provided 14 grams of protein, 8 grams from soy
and 6 grams from semolina. Each Chocolate Daydream™ shake contained 20 g soy protein. Each Peanut Butter Chocolate Pal™ bar contained 16
g soy protein. The soy protein chips contained 7 g soy protein per serving
Table 1: Mass and nutrient contents of the test portions of the glucose reference and the two test foods in Experiment 1.
Test Food Portion Size (g)
1
Energy (kJ
[Cal]) {kj/100 g
of product}
Protein (g)
2
{g/
100 g of prod-
uct}
Fat (g) {g/100 g
of product}
Available
Carbohydrate
(g) {g/100 g of
product}
Sugars (g) {g/
100 g of
product}
Fiber (g) {g/100
g of product}
Glucose
(Reference Food)
10.0 g glucose 250
g water
160 [38.2] 0.0 0.0 10.0 10.0 0.0
Chocolate
Raspberry Zing™
bar
150.0 2205 [526.7]
{1,470}
45.0 {30.4} 12.5 {8.4} 10.0 {7.7} 0.0 {0.0} < 2.0 {1.3}
Chocolate
Daydream™ shake
– sucralose
70.0 g powder
500.0 g water
1092 [260.8]
{1,551}
40.0 {57.1} 5.0 {7.1} 10.0 {14.3} 2.0 {2.9} 4.0 {5.7}
1
The amount of water listed for some products is the amount of water needed to prepare that product. This volume is in addition to the 250 g of
water consumed with every product as part of the test methodology.
2
All of the protein in the products above is derived from soy. Each Chocolate Raspberry Zing™ bar contained 21 g soy protein. Each Chocolate
Daydream™ shake contained 20 g soy protein.
Nutrition Journal 2006, 5:35 http://www.nutritionj.com/content/5/1/35
Page 4 of 10
(page number not for citation purposes)
Statistical Analyses
Sample size calculations (90% power, level of significance
= 0.05) using data from published GI studies indicated
that a minimum of eight study subjects would be needed
to detect significant differences among the GI values of the
reference and test foods. Analysis of variance (ANOVA)
and the Fisher PLSD test for multiple comparisons were
used to determine significant differences between the test
foods' mean GI and II values. Statistical analyses were
conducted using Statview Student™ software (version 4,
Abacus Concepts Inc., Berkley, CA, USA). Significance was
assumed at P < 0.05.
Results
Experiment 1: Low-Carbohydrate Soy Foods
The mean blood glucose response curves for the reference
and two test foods are shown in Figure 1. The reference
food produced a much larger rise in blood glucose during
the first 30 minutes and a greater overall glycemic
response than the two test foods. The two test foods pro-
duced slightly different glucose response curves with the
Chocolate Raspberry Zing™ bar producing a higher glyc-
emic response than the Chocolate Daydream™ sucralose
shake. However, both foods produced very low glycemic
response curves.
The mean plasma insulin response curves for the reference
and two test foods are shown in Figure 2. The plasma insu-
lin responses observed for the reference food and the test
foods showed a similar profile to their concurrent blood
glucose responses. The reference food produced the high-
est peak plasma insulin concentration and the largest
overall plasma insulin response, followed by the Choco-
late Raspberry Zing™ bar and the Chocolate Daydream™
sucralose shake, respectively.
Blood glucose response curves of low-carbohydrate soy products in Experiment 1Figure 1
Blood glucose response curves of low-carbohydrate soy products in Experiment 1. The mean blood glucose
response curves for the equal-carbohydrate portions of the reference food (glucose) and the two soy-based, low-carbohydrate
food products tested in Experiment 1. Data are expressed as the change in blood glucose concentration from the fasting base-
line concentration. Bars for each data point represent standard error of the means (SEM).
Nutrition Journal 2006, 5:35 http://www.nutritionj.com/content/5/1/35
Page 5 of 10
(page number not for citation purposes)
Experiment 2: Other Soy Foods
The mean blood glucose response curves for the reference
and the four test foods are shown in Figure 3. Similar to
the results observed in Experiment 1, the reference food
produced a large rise in blood glucose during the first 30
minutes and the greatest overall glycemic response. The
four test foods varied in their peak blood glucose concen-
trations and their overall glycemic responses. Among the
test foods, the baked soy protein chips produced the larg-
est glycemic response followed by the Peanut Butter Choc-
olate Pal™ bar, the soy spaghetti, and the Chocolate
Daydream™ fructose shake.
The mean plasma insulin response curves for the reference
and the four test products are shown in Figure 4. The
foods' average plasma insulin responses were similar to
their respective mean plasma glucose responses. The refer-
ence food produced the largest plasma insulin response,
followed by the four test foods in the same order as their
glycemic responses.
Glycemic and Insulin Indices
The mean GI value of the glucose reference was signifi-
cantly greater (P < 0.001) than the mean GI values of each
of the test foods with the exception of the baked soy pro-
tein chips (Figure 5). The mean GI value of the soy protein
chips was not different from the glucose reference, but was
significantly greater (P < 0.001) than the mean GI values
for the other five test foods. Despite a high GI value, the
soy protein chips had only a medium GL value due to the
small serving size and relatively low carbohydrate level
(Table 3).
The mean II value of the glucose reference was signifi-
cantly greater (P < 0.001) than the mean II values of each
of the six test foods (Figure 5). The mean II of the soy pro-
tein chips was significantly higher (P < 0.001) than the
Plasma insulin response curves of low-carbohydrate soy products in Experiment 1Figure 2
Plasma insulin response curves of low-carbohydrate soy products in Experiment 1.The mean plasma insulin
response curves for the equal-carbohydrate portions of the reference food (glucose) and the two soy-based, low-carbohydrate
food products tested in Experiment 1. Data are expressed as the change in plasma insulin concentration from the fasting base-
line concentration. Bars for each data point represent standard error of the means (SEM).
Nutrition Journal 2006, 5:35 http://www.nutritionj.com/content/5/1/35
Page 6 of 10
(page number not for citation purposes)
mean II values for the soy spaghetti, Chocolate Day-
dream™ fructose shake, Chocolate Raspberry Zing™ bar,
and the Chocolate Daydream™ sucralose shake. The mean
II values for the Peanut Butter Chocolate Pal™ bar and the
Chocolate Raspberry Zing™ bar were both significantly
greater (P < 0.001) than the mean II values for the Choc-
olate Daydream™ fructose and Chocolate Daydream™
sucralose shakes.
Discussion
The results of this study demonstrate that 5 of the 6 soy
food products tested have a low GI value (GI 55). Of the
6 products tested, only the baked soy protein chips had a
high GI value (GI > 70). However, when the amount of
available carbohydrates in one serving of the soy protein
chips was used to calculate a GL value, the soy protein
chips had a medium GL value. The other products tested
also were either low or medium GL foods.
An increasing body of evidence suggests that the GI and/
or GL values of foods impact human health (see
[5,6,12,32,33] for reviews). Low-GI diets have been
shown to improve glycemic control in diabetic [14,15]
and non-diabetic [16,17] subjects. Diet impacts the inci-
dence of type 2 diabetes and the regulation of dietary car-
bohydrate has taken on a prominent role in dietary
control of this chronic disease. Two recent meta-analyses
[13,34] reported that consumption of low-GI foods rather
than high-GI foods appears to modestly improve glycemic
control by reducing plasma cholesterol, fructosamine,
and hemoglobin A
1C
(Hb
A1c
) levels.
A number of studies suggest that high GI/GL diets may
increase CVD risk [7-9], while several others indicate that
low GI diets may reduce some CVD risk factors
[9,14,15,20-23]. Meta-analysis results indicated that low
GI diets significantly reduced total cholesterol (average
Blood glucose response curves of soy products in Experiment 2Figure 3
Blood glucose response curves of soy products in Experiment 2.The mean blood glucose response curves for the
equal-carbohydrate portions of the reference food (glucose) and the four soy-based food products tested in Experiment 2.
Data are expressed as the change in blood glucose concentration from the fasting baseline concentration. Bars for each data
point represent standard error of the means (SEM).
Nutrition Journal 2006, 5:35 http://www.nutritionj.com/content/5/1/35
Page 7 of 10
(page number not for citation purposes)
reduction = 0.17 mmol/L; P = 0.03) and HbA
1c
(average
reduction after 12 weeks = 0.45%; P = 0.02) compared to
high GI diets [35]. These data suggest that dietary GI may
improve some, but not all markers of cardiovascular dis-
ease risk.
Few studies have examined the effect of low GI diets on
weight loss; however, there is some evidence that low GI
diets may be beneficial [33]. In obese women, an energy-
restricted, low GI diet significantly increased weight loss
compared to an energy-restricted, high GI diet [18]. Spieth
and co-workers [19] demonstrated that an ad libitum low
GI diet significantly (P < 0.05) reduced BMI to a greater
extent than did an energy-restricted, low fat diet. A recent
study demonstrated that dietary GI was inversely associ-
ated with thigh intramuscular fat while GL was inversely
associated with visceral abdominal fat in men [36].
Despite the potential benefits of low-GI diets on weight
loss, a number of studies report no effect on weight loss
[37,38].
Soy protein is a high quality protein that has been exten-
sively studied. The quality of soy protein has been
assessed through several metabolic studies of nitrogen
balance [39-41], which have demonstrated that soy pro-
tein supports nitrogen balance on par with beef and milk
proteins. One recent study reported that amino acids from
soy protein appear in the serum sooner, but that this may
lead to a more rapid breakdown of the amino acids in the
liver [42].
Dietary soy consumption has been shown to have benefi-
cial effects on several aspects of human health, including
the diseases potentially influenced by dietary GI levels
[43-45]. Soy consumption has been reported to modestly
improve plasma lipid profiles [46,47], improve bone
Plasma insulin response curves of soy products in Experiment 2Figure 4
Plasma insulin response curves of soy products in Experiment 2.The mean plasma insulin response curves for the
equal-carbohydrate portions of the reference food (glucose) and the four soy-based food products tested in Experiment 2.
Data are expressed as the change in plasma insulin concentration from the fasting baseline concentration. Bars for each data
point represent standard error of the means (SEM).
Nutrition Journal 2006, 5:35 http://www.nutritionj.com/content/5/1/35
Page 8 of 10
(page number not for citation purposes)
health [48], help reduce menopausal symptoms [49], and
slightly reduce the risk of breast [50] and prostate cancers
[51]. The health benefits of dietary soy have been attrib-
uted to its isoflavones as well as to the biological actions
of its constituent proteins. However, an additional means
Table 3: Glycemic index and glycemic load
1
values for the six tested soy food products
Glycemic Index Glycemic Load
Test Food Carbohydrate Tested (g) Value ± SEM
2
Category
3
Carbohydrates/Serving (g) Value Category
4
Chocolate Daydream™ shake – sucralose 10.0 25.00 ± 4.28 Low 5 1.25 Low
Chocolate Daydream™ shake – fructose 25.0 32.73 ± 4.41 Low 34 11.13 Medium
Soy spaghetti 25.0 47.03 ± 7.48 Low 33 15.52 Medium
Chocolate Raspberry Zing™ bar 10.0 47.42 ± 4.55 Low 6 2.85 Low
Peanut Butter Chocolate Pal™ bar 25.0 51.82 ± 3.86 Low 31 16.06 Medium
Lightly Salted Sunshine™ soy protein chips 25.0 86.79 ± 7.86 High 13 11.28 Medium
1
Glycemic Load = (GI × net carbohydrates)/100
2
SEM = Standard Error of the Means
3
Glycemic Index Category: Low = 55; Medium = 56 – 69; High = > 70 [54]
4
Glycemic Load Category: Low = 10; Medium = 11 – 19; High = > 20 [54]
Glycemic and insulin index values of tested soy productsFigure 5
Glycemic and insulin index values of tested soy products. The mean (± SEM) glycemic index and insulin index for the
reference food (glucose) and the six tested soy-based food products. The dark bars represent the glycemic index values and
the light bars represent the insulin index values. For the GI values, columns with different superscripts (a, b) are significantly (P
< 0.001) different. Columns representing the II values with different superscripts (w, x, y, z) are significantly different (P <
0.001).
Nutrition Journal 2006, 5:35 http://www.nutritionj.com/content/5/1/35
Page 9 of 10
(page number not for citation purposes)
of providing health benefits may be through the low GI of
soy and soy foods.
The international table of GI and GL values [26] reports
the GI/GL values of a number of soy foods. These values
range from a low GI of 14 for soybeans canned in brine to
a high GI of 115 for a tofu-based frozen dessert [24]. The
Revival Soy
®
products tested in this study fell within this
range and with the exception of the baked soy protein
chips were all within the low GI category. Only a few other
studies have reported on the GI of soy-based foods. Packer
et al. [25] indicated that gluten-free, soy-based, bread had
a high GI value. In contrast, the addition of soy foods has
been shown to lower the GI value of mixed meals [27].
Similarly, the replacement of unrefined wheat flour with
soy flour lowers the GI value of parantha, an Indian snack
food [52]. Similar to the Revival Soy
®
bars tested in the
current study, other snack bars containing soy have been
shown to have low to medium GI values [53]. Previous
data report that the GI values of spaghetti ranges from 27
– 68. The Revival Soy
®
thin spaghetti had a GI value = 47,
similar to other spaghetti products. Overall, these studies
indicate that soy-based foods generally have a low to
medium GI value and would be suitable for individuals
concerned with regulating blood glucose and insulin lev-
els.
The ingredients and form of a food product affect its GI
value. For example, while soybeans have a low GI value,
the use of high GI ingredients in soy foods can increase
the GI value of the final product. This was likely the case
with the baked soy protein chips. The baked soy protein
chips contain potato starch and potatoes have a high GI
value [33]. Additionally, the baked soy protein chips have
a puffed physical form, which may lead to high GI values.
Similar to planning diets with types of fat and protein in
mind, types of carbohydrates should also be considered
since carbohydrate types influence the GI. The substitu-
tion of high GI ingredients with low GI ingredients in
food products like the baked soy protein chips may help
keep the final GI value down.
In addition to the effects of form and content of foods on
the GI value, consumption of other foods with low GI
foods can affect the overall GI value of meal. Sugiyama et
al. [34] demonstrated that adding soybean products
(miso, natto, and ground soybean) lowered the GI of
white rice by 20 – 40%. However, further studies are
required before any conclusions can be drawn.
Conclusion
With the apparent resurgence of interest in low-GI diets
for weight loss and health benefits, it is important that
information on the GI value of foods is available. There-
fore, we conducted the current study to determine the GI
value of a small variety of commercially available soy
foods. The results of the current study demonstrate that
soy food products generally have low GI values and low to
medium GL values. Improvements in ingredient selection
and usage may further improve glycemic responses to soy
foods. The low GI of soy foods appears to be an additional
benefit of soy for human health and suggests that soy
foods are an appropriate part of diet plans intended to
improve control over blood glucose and insulin levels.
Competing interests
Robert M. Blair is the Research Manager of Physicians
Pharmaceuticals, Inc. E. C. Henley is a consultant to Phy-
sicians Pharmaceuticals, Inc. Aaron Tabor is the CEO and
Medical Research Director of Physicians Pharmaceuticals,
Inc.
Authors' contributions
RMB prepared the manuscript. ECH and AT participated
in the coordination and design of the study and assisted
with manuscript preparation.
Acknowledgements
This study was funded by Physicians Pharmaceuticals, Inc. and conducted via
contract research at the University of Sydney, Australia.
References
1. Jenkins DJ, Wolever TM, Taylor RH, Barker H, Fielden H, Baldwin JM,
Bowling AC, Newman HC, Jenkins AL: Glycemic index of foods: a
physiological basis for carbohydrate exchange. Am J Clin Nutr
1981, 34:362-366.
2. Wolever TMS, Jenkins DJA, Jenkins AL, Josse RG: The glycemic
index: methodology and clinical implications. Am J Clin Nutr
1991, 54:846-854.
3. Salmeron J, Ascherio A, Rimm EB, Colditz GA, Spiegelman D, Jenkins
DJ, Stampfer MJ, Wing AL, Willett WC: Dietary fiber, glycemic
load, and risk of NIDDM in men. Diabetes Care 1997, 20:545-50.
4. Salmeron J, Manson JE, Stampfer MJ, Colditz GA, Wing AL, Willett
WC: Dietary fiber, glycemic load, and risk of non-insulin-
dependent diabetes mellitus in women. JAMA 1997,
277:472-477.
5. Augustin LS, Franceschi S, Jenkins DJA, Kendall CWC, La Vecchia C:
Glycemic index in chronic disease: a review. Eur J Clin Nutr
2002, 56:1049-1071.
6. Ludwig DS: The glycemic index: physiological mechanisms
relating to obesity, diabetes, and cardiovascular disease.
JAMA 2002, 287:2414-2423.
7. Liu S, Willett WC, Stampfer MJ, Hu FB, Franz M, Sampson L, Hen-
nekens CH, Manson JE: A prospective study of dietary glycemic
load, carbohydrate intake, and risk of coronary heart disease
in US women. Am J Clin Nutr 2000, 71:1455-1461.
8. Stampfer MJ, Hu FB, Manson JE, Rimm EB, Willett WC: Primary
prevention of coronary heart disease in women through diet
and lifestyle. New Engl J Med 2000, 343:16-22.
9. Amano Y, Kawakubo K, Lee JS, Tang AC, Sugiyama M, Mori K: Cor-
relation between dietary glycemic index and cardiovascular
disease risk factors among Japanese women. Eur J Clin Nutr
2004, 58:1472-1478.
10. Hodge AM, English DR, O'Dea K, Giles GG: Glycemic index and
dietary fiber and the risk of type 2 diabetes. Diabetes Care 2004,
27:2701-2706.
11. Schulze MB, Liu S, Rimm EB, Manson JE, Willett WC, Hu FB: Glyc-
emic index, glycemic load, and dietary fiber intake and inci-
dence of type 2 diabetes in younger and middle-aged
women.
Am J Clin Nutr 2004, 80:348-356.
Nutrition Journal 2006, 5:35 http://www.nutritionj.com/content/5/1/35
Page 10 of 10
(page number not for citation purposes)
12. Colombani PC: Glycemic index and load – dynamic dietary
guidelines in the context of diseases. Physiol Behav 2004,
83:603-610.
13. Opperman AM, Venter CS, Oosthuizen W, Thompson RL, Vorster
HH: Meta-analysis of the health effects of using the glycaemic
index in meal-planning. Br J Nutr 2004, 92:367-381.
14. Jarvi AE, Karlstrom BE, Granfeldt YE, Bjorck IE, Asp NG, Vessby BO:
Improved glycemic control and lipid profile and normalized
fibrinolytic activity on a low-glycemic index diet in type 2 dia-
betic patients. Diabetes Care 1999, 22:10-18.
15. Giacco R, Parillo M, Rivellese AA, Lasorella G, Giacco A, D'Episcopo
L, Riccardi G: Long-term dietary treatment with increased
amounts of fiber-rich low-glycemic index natural foods
improves blood glucose control and reduces the number of
hypoglycemic control in children with type 1 diabetes. Diabe-
tes Care 2000, 23:1461-1466.
16. Jenkins DJ, Wolever TM, Collier GR, Ocana A, Rao AV, Buckley G,
Lam Y, Mayer A, Thompson LU: Metabolic effects of a low-glyc-
emic-index diet. Am J Clin Nutr 1987, 46:968-975.
17. Frost G, Keogh B, Smith D, Akinsanya K, Leeds S: The effect of low-
glycemic carbohydrate on insulin and glucose response in
vivo and in vitro in patients with coronary heart disease.
Metabolism 1996, 45:669-672.
18. Slabber M, Barnard HC, Kuyl JM, Dannhauser A, Schall R: Effects of
a low-insulin-response, energy-restricted diet on weight loss
and plasma insulin concentrations in hyperinsulinemic obese
females. Am J Clin Nutr 1994, 60:48-53.
19. Spieth LE, Harnish JD, Lenders CM, Raezer LB, Pereira MA, Hangen
SJ, Ludwig DS: A low-glycemic index diet in the treatment of
pediatric obesity. Arch Pediatr Adolesc Med 2000, 154:947-951.
20. Jenkins DJ, Wolever TM, Kalmusky J, Guidici S, Giordano C, Patten R,
Wong GS, Bird JN, Hall M, Buckley G: Low-glycemic index diet in
hyperlipidemia: use of traditional starchy foods. Am J Clin Nutr
1987, 46:66-71.
21. Luscombe ND, Noakes M, Clifton PM:
Diets high and low in glyc-
emic index versus high monounsaturated fat diets: effects on
glucose and lipid metabolism in NIDDM. Eur J Clin Nutr 1999,
53:473-478.
22. Ford ES, Liu S: Glycemic index and serum high-density lipopro-
tein cholesterol concentration among US adults. Arch Intern
Med 2001, 161:572-576.
23. Liu S, Manson JE, Buring JE, Stampfer MJ, Willett WC, Ridker PM:
Relation between a diet with a high glycemic load and
plasma concentrations of high-sensitivity C-reactive protein
in middle-aged women. Am J Clin Nutr 2002, 75:492-498.
24. Bukar J, Mezitis NHE, Saitas V, Pi-Sunyer FX: Frozen desserts and
glycaemic response in well-controlled NIDDM patients. Dia-
betes Care 1990, 13:382-385.
25. Packer SC, Dornhurst A, Frost GS: The glycaemic index of a
range of gluten-free foods. Diabet Med 2000, 17:657-660.
26. Foster-Powell K, Holt SHA, Brand-Miller JC: International table of
glycemic index and glycemic load values: 2002. Am J Clin Nutr
2002, 76:5-56.
27. Sugiyama M, Tang AC, Wakaki Y, Koyama W: Glycemic index of
single and mixed meal foods among common Japanese
foods. Eur J Clin Nutr 2003, 57:743-752.
28. Joint FAO/WHO Report: Carbohydrates in Human Nutrition.
FAO Food and Nutrition, Paper #66. Rome: FAO; 1998.
29. Brouns F, Bjorck I, Frayn KN, Gibbs AL, Lang V, Slama G, Wolever
TMS: Glycaemic index methodology. Nutr Res Rev 2005,
18:145-171.
30. Wolever TM, Vorster HH, Bjorck I, Brand-Miller J, Brighenti F, Mann
JI, Ramdath DD, Granfeldt Y, Holt S, Perry TL, Venter C, Xiaomei W:
Determination of the glycaemic index of foods: interlabora-
tory study. Eur J Clin Nutr 2003, 57:475-482.
31. Brand-Miller JC, Thomas M, Swan V, Ahmad ZI, Petocz P, Colagiuri S:
Physiological validation of the concept of glyemic load in lean
young adults. J Nutr 2003, 133:2728-2732.
32. Dickinson S, Brand-Miller J: Glycemic index, postprandial glyc-
emia and cardiovascular disease.
Curr Opin Lipidol 2005,
16:69-75.
33. Brand-Miller JC, Holt SHA, Pawlak DB, McMillan J: Glycemic index
and obesity. Am J Clin Nutr 2002, 76:281S-285S.
34. Brand-Miller J, Hayne S, Petocz P, Colagiuri S: Low-glycemic index
diets in the management of diabetes: a meta-analysis of ran-
domized controlled trials. Diabetes Care 2003, 26:2261-2267.
35. Kelly S, Frost G, Whittaker V, Summerbell C: Low glycaemic index
diets for coronary heart disease. The Cochrane Database of Sys-
tematic Reviews 2004. Art. No.: CD004467.pub2. DOI: 10.1002/
14651858.CD004467.pub2
36. Sahyoun NR, Anderson AL, Kanaya AM, Koh-Banerjee P, Kritchevsky
SB, de Rekeneire N, Tylavsky FA, Schwartz AV, Lee JS, Harris TB:
Dietary glycemic index and load, measures of glucose
metabolism, and body fat distribution in older adults. Am J
Clin Nutr 2005, 82:547-552.
37. Wolever TMS, Jenkins DJA, Vuksan V, Jenkins AL, Wong GS, Joss RG:
Beneficial effect of low-glycemic index diet in overweight
NIDDM subjects. Diabetes Care 1992, 15:562-564.
38. Raatz SK, Torkelson CJ, Redmon JB, Reck KP, Kwong CA, Swanson
JE, Liu C, Thomas W, Bantle JP: Reduced glycemic index and gly-
cemic load diets do not increase the effects of energy restric-
tion on weight loss and insulin sensitivity in obese men and
women. J Nutr 2005, 135:2387-2391.
39. Young VR, Puig M, Queiroz E, Scrimshaw NS, Rand WM: Evaluation
of the protein quality of an isolated soy protein in young
men: relative nitrogen requirements and effects of methio-
nine supplementation. Am J Clin Nutr 1984, 39:16-24.
40. Baglieri A, Mahe S, Zidi S, Huneau JF, Thuillier F, Marteau P, Tome D:
Gastro-jejunal digestion of soya-bean-milk protein in
humans. Br J Nutr 1994, 72:519-532.
41. Mariotti F, Mahe S, Benamouzig R, Luengo C, Dare S, Gaudichon C,
Tome D: Nutritional value of [15N]-soy protein isolate
assessed from ileal digestibility and postprandial protein uti-
lization in humans. J Nutr 1999, 129:1992-1997.
42. Bos C, Metges CC, Gaudichon C, Petzke KJ, Pueyo ME, Morens C,
Everwand J, Benamouzig R, Tome D:
Postprandial kinetics of die-
tary amino acids are the main determinant of their metabo-
lism after soy or milk protein ingestions in humans. J Nutr
2003, 133:1308-1315.
43. Bhathena SJ, Velasquez MT: Beneficial role of dietary phytoes-
trogens in obesity and diabetes. Am J Clin Nutr 2002,
76:1191-1201.
44. Hasler CM: The cardiovascular effects of soy products. J Cardi-
ovasc Nurs 2002, 16:50-63.
45. Sarkar FH, Li Y: The role of isoflavones in cancer chemopre-
vention. Front Biosci 2004, 9:2714-2724.
46. Zhan S, Ho SC: Meta-analysis of the effects of soy protein con-
taining isoflavones on the lipid profile. Amer J Clin Nutr 2005,
81:397-408.
47. Merz CNB, Johnson BD, Braunstein GD, Pepine CJ, Reis SE, Paul-Lab-
rador M, Hale G, Sharaf BL, Bittner V, Sopko G, Kelsey SF, for the
Women's Ischemia Syndrome Evaluation Study Group: Phytoestro-
gens and lipoproteins in women. J Clin Endocrinol Metab 2006,
91:2209-2213.
48. Messina M, Ho S, Alekel DL: Skeletal benefits of soy isoflavones:
a review of the clinical trial and epidemiologic data. Curr Opin
Clin Nutr Metab Care 2004, 7:649-658.
49. Howes LG, Howes JB, Knight DC: Isoflavone therapy for meno-
pausal flushes: a systematic review and meta-analysis. Matu-
ritas 2006, 55:203-211.
50. Trock BJ, Hilakivi-Clarke L, Clarke R: Meta-analysis of soy intake
and breast cancer risk. J Natl Cancer Inst 2006, 98:459-471.
51. Yan L, Spitznagel EL: Meta-analysis of soy food and risk of pros-
tate cancer in men. Int J Cancer 2005, 117:667-669.
52. Jain M, Verma N: Impact of incorporating soya fibre and proc-
essed soyabean flours on the glycaemic index of parantha.
Asia Pac J Clin Nutr 2004, 13:S97.
53. Wolever TMS, Radmard R: Glycemic index of popular nutrition
and energy bars. J Amer Dietetic Assoc 2002, 102:A78.
54. Sydney University Glycemic Index Research Service [http://
www.glycemicindex.com]
... The glycaemic index (GI) level plays the biggest role, where the highest one produces the most impact on BG levels while also the fastest. An effect of different levels of GI in food intake towards BG can be seen in Figure 2.5 [21]. Glucose (liquid), chocolate bar and sucralose shake (both solid with low GI) are consumed separately on different days and each having the BG response curve measured after a specific time. ...
... Following the arrangement of objects from the initial reference point, the distances of all subsequences are calculated and stored in a table called Dist, which is also sorted [65]. Objects that are worse than the best-so-far are disregarded for computing of the actual distance of the two motif candidates (reflected in lines [20][21]. After the arrangements, the linear ordering is scanned and true distances between adjacent pairs are measured, followed by an update to the best-so-far. ...
... Glucose response curve of different level of carbohydrate foods[21]. ...
Thesis
Type 1 diabetes is caused by the destruction of insulin-producing mechanisms in the pancreas, resulting in uncontrolled blood glucose concentration (BGC). Previous modelling techniques have generally been marginally successful in producing accurate predictions, in part due to the lack of real physiological data from patients. This research considers a Diabetes UK study, which collected free-living data from individuals with Type 1 diabetes using a continuous glucose monitor, activity armband and food and insulin diaries to investigate methods to predict BGC over several hours. A further aim was to improve understanding of the role of physical activity in BGC variations. Initial tests were performed to directly find invisible patterns within the raw meal, insulin, sleep and activity data but no significant correlations that could be beneficial to help develop better predictive models was found. A number of methods of modelling BGC in Type 1 diabetes are investigated and it was found out that every diabetic subject has his/her own parameter variations, different to the one-size-fits-all reference parameters, hindering improved predictions. As a resolution, an optimization-driven technique is developed for the linear time-invariant ARX class of models, aiming to refine reference constant parameters in glucose and insulin sub-models to suit the very distinct features of each set of Type 1 BGC data. The outcome of this work showed that optimising these values for parameters of digestion in the gut and insulin diffusion in subcutaneous tissue could improve the predictive properties of the ARX models. However, although the prediction was improved over previous research, it was still far from satisfactory due to the non-linearities existing in the BG data. Thus, non-stationary Gaussian Processes was investigated to address the BGC’s non-stationarity and autocorrelation with variations present both intra and inter-patient. This approach was not only able to better describe local blood glucose dynamics in response to carbohydrate and insulin intake, but also permitted the inclusion of physical activity energy expenditure data, an advantage over the previous linear time-invariant ARX. Physiological based-models were then investigated for parameter learning, especially the physical activity data for BGC prediction over several hours. These models showed some predicting capability, highlighting periods of low and high BGC. However, physiological variables analysed were found to differ from estimates in the literature derived from healthy individuals. Therefore, a new semi-empirical compartmental model was developed to better represent the underlying physiology, however resulted in only minimal improvement. Finally, a multi-objective optimizationbased approach, constrained by published upper and lower bounds for the variables, was employed to refine these parameter estimates. This cohort-driven technique was shown to perform the best, exceeding those of the previous physiological-based modelling methods and while it is not limited to only short-term predictions, it achieved better prediction on most of the volunteers’ studied.
... In the current study, the improvement in FBG, insulin and HOMA-IR after the active treatment in the roasted soy-nut group without a change in weight is independent of the action of soy fiber that enhances the feeling of satiety and thus causes an improvement in insulin resistance secondary to weight loss. Soy-nut has a lower glycemic index than TSP, because of the higher phytate and fiber content [52]. ...
... In contrast, some studies did not observe significant improvement in oxidative stress markers. One of which included 25 type 2 diabetic patients with nephropathy who consumed soy milk supplement for 4 weeks [22] and the other which included 43 oophorectomized women who consumed 75 mg/day isoflavones tablet for 12 weeks [52]. The inconsistency between studies could be due to the form in which soy was provided. ...
Article
Full-text available
Background: There are many studies on the health effects of soy, only a few describe the effects of the simultaneous use of two types of soy on multiple components of metabolic syndrome (MetS). The present study was designed to determine the effects of roasted soy-nut and textured soy protein (TSP) intake on clinical and metabolic status of older women with MetS borderline parameters. Method: This randomized, single-blind, controlled clinical trial included 75 women ≥ 60 years old with a diagnosis of MetS based on ATP III criteria. The participants were randomly allocated into three groups of 25 people; soy-nut, TSP and control groups for 12 week. Fasting blood samples were taken at the beginning and end of the trial to compare the metabolic responses. All participants provided three dietary records and physical activity records during the intervention. We used the Kolmogorov-Smirnov, ANOVA, ANCOVA, paired-t test, and the Generalized Linear Model (GLM) repeated measures analysis. Results: Dietary intake and physical activity of the participants in two groups were not significantly different. After 12 weeks of intervention the participants who received soy-nut had a significant decrease in total cholesterol (TC) (p < 0.001), low density lipoprotein, very low density lipoprotein, apolipoprotein B100, fasting blood glucose, insulin (p < 0.05), HOMA-IR, malondialdehyde (MDA) (p < 0.01) level. Morever, a significant increase in total antioxidant capacity (TAC) (p < 0.01) level compared with the control group. At the same time, the TSP brought significant decrease only in TC, insulin, MDA (p < 0.05) level and a significant increase in total TAC (p < 0.05) level. We did not find any significant effect in intervention groups, on apolipoprotein AI, triglyceride (TG), high density lipoprotein (HDL-C), TG/HDL, C-reactive protein and fibrinogen levels after intervention. Conclusion: Short-term intakes of roasted soy-nut and TSP have shown to improve the lipid profiles, markers of glucose intolerance and oxidative stress; although the roasted soy-nut was more effective than TSP. Therefore, a moderate daily intake of roasted soy-nut as snacks or TSP as a meal complement by individuals with borderline parameters of MetS can be a safe and a practical modality to avoid the progression of the disease as well as to limit the side effects of drug intake.Trial registration MUBABOL.REC.1388.1.
... Their consumption, by virtue of their physiologically active components, should provide health benefits beyond basic nutrition [28]. Since pasta formulation could affect the glycemic response after consumption, and therefore, its GI, beyond the processing method [29][30][31], a large number of human intervention studies have investigated the GI of enriched pasta products [18,21,[32][33][34]. Thus, since the GI represents one of the most important parameters considered for evaluating the quality of dietary carbohydrates, this study aimed to gather the GI values of pasta products (pp) published in the literature until 2020. ...
Article
Full-text available
Durum wheat pasta is considered a low-glycemic index (GI) food. In recent years, the interest in developing enriched pasta has increased. Since both the formulation and processing technologies may affect the GI, this study aimed to investigate the GI values of pasta products (pp) reported in the literature until 2020. GI values of pp analyzed following the ISO guidelines were included in this survey. A total of 95 pp were identified and, according to their formulation, classified into 10 categories (n, mean GI): category n 1: 100% refined wheat (35, 55); category n 2: 100% whole wheat (6, 52); category n 3: other cereal-based products (8, 52); category n 4: containing egg (5, 52); category n 5: gluten free (11, 60); category n 6: containing legumes (9, 46); category n 7: noodles and vermicelli (9, 56); category n 8: containing vegetable or algae (6, 51); category n 9: containing other ingredients (5, 37); category n 10: stuffed (1, 58). Overall, pasta is confirmed to be a medium–low-GI food, even if a high variability among or within each category emerged. The formulation of enriched pp able to elicit a controlled glycemic response could represent a strategy to improve the nutritional value of pasta.
... The baking industry can realize the benefits of soy protein. Soy flour helps to reduce the glycemic index of all baked food and provides a way to improve blood sugar control, reduce carbohydrate load improves foods protein content (Blair et al., 2006) In the present study, soy chunks were used with standardizing suitable recipes incorporated and developed with the same. The products formulated with soy chunks namely soy nuggets and soy kurma had 3.8 and 3.6 overall acceptability score on sensory evaluation. ...
Article
Soybean differs from other cereals and legumes by containing the highest amount of complete protein. Tofu, soy milk, soy meat are nonfermented products of soybean. Soy contains chemical compounds are unique because of its high concentration of isoflavones, a type of plant estrogen. Women entering the menopausal stage are recommended to consume soya bean because this plant estrogen would benefit them. The study aims to inculcate the consumption of soy products through standardized recipes. The popular soy milk product is Tofu. It is prepared by curdling fresh hot soy milk with a coagulant. On milling, soybean yielded a nutritious product called Soy flour, which is available in two types namely full-fat soy flour (FFSF) and defatted soy flour (DFSF). When the soybean oil is extracted, the by-product called Soy Chunks is produced. In current study, soy products namely Tofu, Defatted Soy Flour, and Soy Chunks were used to standardize few recipes by incorporating them in commonly consumed recipes or using them wholly. Further, for this, Tofu with pasta and sandwich, Chunks in kurma and nuggets, DFSF with besan omelette, and cakes were incorporated and standardized. Along with this, organoleptic evaluations were carried out for the developed products and were standardized. The nutritive values for DFSF and storage stability of standardized cakes were also carried out.
... Experimentally, the intake of soy and fermented milk proteins, especially in the morning, positively alters the structure of gut microbiota and results in increased production of health promoting nutrients (e.g., short-chain fatty acids) by bacteria of the colon [96]. Several lines of evidence show that soy foods are low GI, and they can lower blood glucose level in healthy adults [97,98] and in T2DM patients [99]. The anti-hyperglycemic effects of soy appear to be gender specific. ...
Article
Full-text available
Gestational diabetes mellitus (GDM) is a common pregnancy-related condition afflicting 5-36% of pregnancies. It is associated with many morbid maternal and fetal outcomes. Mood dysregulations (MDs, e.g., depression, distress, and anxiety) are common among women with GDM, and they exacerbate its prognosis and hinder its treatment. Hence, in addition to early detection and proper management of GDM, treating the associated MDs is crucial. Maternal hyperglycemia and MDs result from a complex network of genetic, behavioral, and environmental factors. This review briefly explores mechanisms that underlie GDM and prenatal MDs. It also describes the effect of exercise, dietary modification, and intermittent fasting (IF) on metabolic and affective dysfunctions exemplified by a case report. In this patient, interventions such as IF considerably reduced maternal body weight, plasma glucose, and psychological distress without any adverse effects. Thus, IF is one measure that can control GDM and maternal MDs; however, more investigations are warranted.
... Soy is a food with low GI (Blair et al. 2006) and a source of protein, fibre, vitamin, mineral, good fat, isoflavone and phytoestrogen (Lokuruka 2010). Several studies suggested that soy is both beneficial in lowering the risk of type 2 diabetes mellitus in healthy subjects (Mueller et al. 2012) and improving glucose response in patients with type 2 diabetes mellitus (Sun et al. 2017). ...
Article
Full-text available
This study aimed to determine the Glycaemic Index (GI), Glycaemic Response (GR) and Glycaemic Load (GL) of soy flour-based snack bars in healthy volunteers. An open label randomized controlled trial with crossover study design was done involving eighty adults aged 18‒50 years. The glycaemic index was calculated using Incremental Area Under the Blood Glucose Response Curve (iAUC). Friedman’s test was used to determine difference of glucose iAUC between WF and SF. Wilcoxon test was used to determine difference of blood glucose peak, time to blood glucose peak, GI and GR between snack bars. The result observed that median (Q1‒Q3) of GI were 88.4 (42.3‒115.8); WF: 36.6 (21.8‒47.9) (Product SF3, Banana); 36.3 (18.9‒49.2) (Product SF6, Crispy White Chocolate Macadamia); 29.9 (22.0‒43.3) (Product SF5, Crispy Vanilla); 25.9 (17.8‒35.4) (Product SF4; Strawberry); 20.2 (15.3‒22.2) (Product SF1, Almond Chocolate); and 7.1 (5.4‒17.0) (Product SF2, Raisin Almond). We found that GL of WF was (17.7). While, the GL of snack bars made from SF were 4.9 (Product SF3, Banana), 4.1 (Product SF4, Strawberry), 1.9 (Product SF1, Almond Chocolate); 1.8 (Product SF6, Crispy White Chocolate Macadamia), 1.6 (Product SF5, Crispy Vanilla), and 0.9 (Product SF2, Raisin Almond). Friedman statistical test showed significant differences on the blood glucose iAUC between SF and WF (p<0.001). SF snack bar showed different GR results, where the area of each products (SF1‒SF6) curve was significantly lower than WF. Based on Wilcoxon test, the GI and GR of SF were significantly lower than WF (p<0.05). In conclusion, SF snack bars can be classified as a low GI-source snack bar with a low category of glycaemic load; and had relatively high fibre, protein, and fat content which contributed to a lower GI value. Thus, it is a potential snacks alternative for people with blood glucose concerns.
... Carbohydrates present in soy are slowly digested and absorbed, thus produce slow and small rises in blood sugar and helps in lowering the glycemic index. 26,27 Since high amount of soy added causes to decrease the consumer acceptance, so addition must be at level where maximum consumer accepts the product while also ensuring the health benefit for the human body 28. In our experiments, 15% incorporation of soy has high acceptance with significantly lower the GI value (p<0.05). ...
Article
Full-text available
Low glycemic index foods have been associated with several health benefits. Similarly, soy-based food products have an increasing demand in the market due to its high nutritional value. The study was conducted to produce high-quality protein-enriched bread with low glycemic index (GI) value. The fortification of bread was done with soy flour in our study. The proximate analysis, sensory evaluation and the GI value of the bread sample were determined.The crude protein, crude fiber, fat and ash content of the bread progressively increased with the addition of soy flour where 20% soy bread having the highest values as 14.5%, 0.7%, 5% and 2.1%, respectively and control bread having lowest values as 9.2%, 0.2%, 1.8%, and 1.7%, respectively. The sensory evaluation of bread showed no significant differences in crust, shape, internal texture, appearance and general acceptance where the aroma and the taste of bread samples were significantly different from the control bread. Taste of bread had a low score of 5.81 at 20% soy substitution bread and highly differed with control bread (p<0.01). The GI values were significantly lowered by 15% (p<0.05) and 20% (p<0.01) soy sample bread. The average GI value of Control, 10%, 15% and 20% soy substitution bread were 60.4, 49.98, 44.37 and 39.19 respectively. Glycemic Index decreased from 18% to 35% with the incorporation of soy flour (10% to 20%) in the bread sample. The soy flour treated breads were found nutritionally superior compared to soy untreated food.
Chapter
Functional foods are the foods claimed that have additional health benefits beyond their basic nutritional values, and functional food components are bioactive, potentially beneficial compounds that are found either naturally in foods or added to them as functional ingredients. Some important functional food components are carotenoids, isothiocyanates, soluble and insoluble dietary fiber, phenolic acids, fatty acids, plant stanols and sterols, flavonoids, polyols, soy protein, prebiotics and probiotics, phytoestrogens, vitamins, and minerals. Most of the functional food components occur mainly in plant foods (whole grains, fruits, and vegetables), however, few functional foods components, such as omega-3, -6, and -9 polyunsaturated fatty acids are also found in animal products (e.g. milk, fermented milk products, and cold-water fish). Evidence suggested that there is a relationship between functional food components and health benefits. Functional food components can be used for the treatment and prevention of different diseases. Biologically active functional food components can reduce the risk of certain non-communicable diseases, such as cancer, type II diabetes, cardiovascular diseases, osteoporosis, inflammation, and lowering of blood cholesterol. Thus, people should consume a wide variety of foods to assure the ingestion of functional food components in their body, such as fatty acids, fiber, carotenoids, flavonoids, prebiotics and probiotics, vitamins, and mineral.
Article
Full-text available
Glycaemic index (GI) testing provides a useful point of comparison between carbohydrate sources. For this comparison to be meaningful, the methods used to determine GI values need to be rigorous and consistent between testing events. This requirement has led to increasing standardization of the GI methodology, with an international standard developed in joint consultation with FAO/WHO (ISO 26642:2010) currently the most up to date document. The purpose of this review is to compare the international standard to methods of published studies claiming to have performed a GI test. This analysis revealed that the international standard permits a wide range of choices for researchers when designing a GI testing plan, rather than a single standardized protocol. It has also been revealed that the literature contains significant variation, both between studies and from the international standard for critical aspects of GI testing methodology. The primary areas of variation include; what glucose specification is used, which reference food is used, how much reference food is given, what drink is given during testing, the blood sampling site chosen and what assay and equipment is used to measure blood glucose concentration. For each of these aspects we have explored some of the methodological and physiological implications of these variations. These insights suggest that whilst the international standard has assisted with framing the general parameters of GI testing, further stan-dardization to testing procedures is still required to ensure the continued relevance of the GI to clinical nutrition.
Article
Full-text available
Keywords: Soybeans; Chickpeas; Lentils; Insulin Resistance; GLUT4; PPARγ; Adipokines; SCFA Producing Bacteria Insulin resistance (IR) stands as an important cause for type2 diabetes mellitus (T2DM) and metabolic syndrome. Right now treatment of IR is done with use of lifestyle modifications and or pharmacological treatment. It has been shown that leguminous plants like soyabeans and pulses that include dried beans dried peas, chickpeas, lentils can decrease IR along with related T2DM. But what is the mechanism of action of these soybeans and pulses in decreasing IR remains elusive. It has been considered that that it is the antioxidant action of these, that is responsible for the same, but there is evidence that independent methods might be there by which insulin sensitivity gets improved. On the bases of published studies using in vivo and in vitro models which represent IR states, the possible mechanism of action of soybeans, chickpeas along with their bioactive compounds are by increasing glucose transport-er4 (GLUT-4), inhibiting adipogenesis by downregulation of peroxisome proliferator activated receptor gamma (PPAR-γ) decreasing adiposity, positively impacting adipokines and increasing short chain fatty acids producing beneficial bacteria in the gut. This review arttempts to explain the detailed mechanism of action of how soybeans and chickpeas act to reduce IR.
Article
Full-text available
Although weight loss can be achieved by any means of energy restriction, current dietary guidelines have not prevented weight regain or population-level increases in obesity and overweight. Many high-carbohydrate, low-fat diets may be counterproductive to weight control because they markedly increase postprandial hyperglycemia and hyperinsulinemia. Many high-carbohydrate foods common to Western diets produce a high glycemic response [high-glycemic-index (GI) foods], promoting postprandial carbohydrate oxidation at the expense of fat oxidation, thus altering fuel partitioning in a way that may be conducive to body fat gain. In contrast, diets based on low-fat foods that produce a low glycemic response (low-GI foods) may enhance weight control because they promote satiety, minimize postprandial insulin secretion, and maintain insulin sensitivity. This hypothesis is supported by several intervention studies in humans in which energy-restricted diets based on low-GI foods produced greater weight loss than did equivalent diets based on high-GI foods. Long-term studies in animal models have also shown that diets based on high-GI starches promote weight gain, visceral adiposity, and higher concentrations of lipogenic enzymes than do isoenergetic, macronutrientcontrolled, low-GI-starch diets. In a study of healthy pregnant women, a high-GI diet was associated with greater weight at term than was a nutrient-balanced, low-GI diet. In a study of diet and complications of type 1 diabetes, the GI of the overall diet was an independent predictor of waist circumference in men. These findings provide the scientific rationale to justify randomized, controlled, multicenter intervention studies comparing the effects of conventional and low-GI diets on weight control.
Article
Full-text available
In order to determine how soya-bean proteins are digested and metabolized in the human intestine before colonic bacterial fermentation and to estimate their true digestibility, the gastro-jejunal behaviour of soya-bean proteins in water and in two other forms (a concentrated soya-bean-protein solution (isolate) and a drink composed of crude soya-bean proteins (soymilk)) was studied in humans. Experiments were carried out in eight healthy volunteers using a double-lumen steady-state intestinal perfusion method with polyethyleneglycol (PEG) as a non-absorbable volume marker. Gastric emptying and N and electrolyte contents of the jejunal digesta were analysed. Gastric half-emptying time (min) of the liquid phase after water ingestion (12·59 (SE 0·12)) was shorter (P < 0.05) than those for soymilk (37·74 (SE 11·57)) and isolate (36·52 (SE 11·23)). Electrolytic balances showed that for all meals, Na+, Cl− and K+ were secreted when Ca2+ was efficiently absorbed from the jejunal lumen. Gastro-jejunal N absorption for isolate and soymilk were 63 and 49% respectively, and were not significantly different from one another; after water ingestion, endogenous N was estimated to be 21 mmol. An estimate of the exogenous: endogenous values for the effluents was obtained from the amino acid compositions of soymilk and effluents after water or soymilk ingestion, indicating that 70% of the total N was exogenous and 30% endogenous. Under these conditions the endogenous fraction represented 31 mmol after soymilk ingestion and the gastro-jejunal N balance indicated that 54% of the soymilk was absorbed. This finding indicates that the true gastro-jejunal digestibility of soya-bean proteins is similar to that of milk proteins.
Article
Full-text available
The glycaemic index (GI) concept was originally introduced to classify different sources of carbohydrate (CHO)-rich foods, usually having an energy content of >80 % from CHO, to their effect on post-meal glycaemia. It was assumed to apply to foods that primarily deliver available CHO, causing hyperglycaemia. Low-GI foods were classified as being digested and absorbed slowly and high-GI foods as being rapidly digested and absorbed, resulting in different glycaemic responses. Low-GI foods were found to induce benefits on certain risk factors for CVD and diabetes. Accordingly it has been proposed that GI classification of foods and drinks could be useful to help consumers make 'healthy food choices' within specific food groups. Classification of foods according to their impact on blood glucose responses requires a standardised way of measuring such responses. The present review discusses the most relevant methodological considerations and highlights specific recommendations regarding number of subjects, sex, subject status, inclusion and exclusion criteria, pre-test conditions, CHO test dose, blood sampling procedures, sampling times, test randomisation and calculation of glycaemic response area under the curve. All together, these technical recommendations will help to implement or reinforce measurement of GI in laboratories and help to ensure quality of results. Since there is current international interest in alternative ways of expressing glycaemic responses to foods, some of these methods are discussed.
Article
To define those patients most likely to benefit from the hypolipidemic effect of low-glycemic-index (GI) traditional starchy foods, 30 hyperlipidemic patients were studied for 3 mo. During the middle month, low-GI foods were substituted for those with a higher GI with minimal change in dietary macronutrient and fiber content. Only in the group (24 patients) with raised triglyceride levels (types IIb, III, and IV) were significant lipid reductions seen: total cholesterol 8.8 +/- 1.5% (p less than 0.001), LDL cholesterol 9.1 +/- 2.4% (p less than 0.001), and serum triglyceride 19.3 +/- 3.2% (p less than 0.001) with no change in HDL cholesterol. The percentage reduction in serum triglyceride related to the initial triglyceride levels (r = 0.56, p less than 0.01). The small weight loss (0.4 kg) on the low-GI diet did not relate to the lipid changes. Low-GI diets may be of use in the management of lipid abnormalities associated with hypertriglyceridemia.
Article
To define those patients most likely to benefit from the hypolipidemic effect of low-glycemic-index (GI) traditional starchy foods, 30 hyperlipidemic patients were studied for 3 mo. During the middle month, low-GI foods were substituted for those with a higher GI with minimal change in dietary macronutrient and fiber content. Only in the group (24 patients) with raised triglyceride levels (types IIb, III, and IV) were significant lipid reductions seen: total cholesterol 8.8 ± 1.5% (p < 0.001), LDL cholesterol 9.1 ± 2.4% (p < 0.001), and serum triglyceride 19.3 ± 3.2% (p < 0.001) with no change in HDL cholesterol. The percentage reduction in serum triglyceride related to the initial triglyceride levels (r = 0.56, p < 0.01). The small weight loss (0.4 kg) on the low-GI diet did not relate to the lipid changes. Low-GI diets may be of use in the management of lipid abnormalities associated with hypertriglyceridemia.
Article
The glycemic index was proposed in 1981 as an alternative system for classifying carbohydrate-containing food. Since then, several hundred scientific articles and numerous popular diet books have been published on the topic. However, the clinical significance of the glycemic index remains the subject of debate. The purpose of this review is to examine the physiological effects of the glycemic index and the relevance of these effects in preventing and treating obesity, diabetes, and cardiovascular disease.
Article
Objective. —To examine prospectively the relationship between glycemic diets, low fiber intake, and risk of non—insulin-dependent diabetes mellitus.Desing. —Cohort study.Setting. —In 1986, a total of 65173 US women 40 to 65 years of age and free from diagnosed cardiovascular disease, cancer, and diabetes completed a detailed dietary questionnaire from which we calculated usual intake of total and specific sources of dietary fiber, dietary glycemic index, and glycemic load.Main Outcome Measure. —Non—insulin-dependent diabetes mellitus.Results. —During 6 years of follow-up, 915 incident cases of diabetes were documented. The dietary glycemic index was positively associated with risk of diabetes after adjustment for age, body mass index, smoking, physical activity, family history of diabetes, alcohol and cereal fiber intake, and total energy intake. Comparing the highest with the lowest quintile, the relative risk (RR) of diabetes was 1.37 (95% confidence interval [CI], 1.09-1.71, Ptrend=.005). The glycemic load (an indicator of a global dietary insulin demand) was also positively associated with diabetes (RR=1.47; 95% CI, 1.16-1.86, Ptrend=.003). Cereal fiber intake was inversely associated with risk of diabetes when comparing the extreme quintiles (RR=0.72,95% CI, 0.58-0.90, Ptrend=.001). The combination of a high glycemic load and a low cereal fiber intake further increased the risk of diabetes (RR=2.50, 95% CI, 1.14-5.51) when compared with a low glycemic load and high cereal fiber intake.Conclusions. —Our results support the hypothesis that diets with a high glycemic load and a low cereal fiber content increase risk of diabetes in women. Further, they suggest that grains should be consumed in a minimally refined form to reduce the incidence of diabetes.
Article
To determine whether low-glycemic index (GI) diets have clinical utility in overweight patients with non-insulin-dependent diabetes mellitus (NIDDM). Six patients with NIDDM were studied on both high- and low-GI diets of 6-wk duration with metabolic diets with a randomized crossover design. Both diets were of similar composition (57% carbohydrate, 23% fat, and 34 g/day dietary fiber), but the low-GI diet had a GI of 58 compared with 86 for the high-GI diet. Small and similar amounts of weight were lost on both diets: 2.5 kg on high-GI diet and 1.8 kg on low-GI diet. On the low-GI diet, the mean level of serum fructosamine, as an index of overall blood glucose control, was lower than on the high-GI diet by 8% (P less than 0.05), and total serum cholesterol was lower by 7% (P less than 0.01). In overweight patients with NIDDM, reducing diet GI improves overall blood glucose and lipid control.