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Insulinemic and glycemic indexes of six starch-rich foods taken alone and in a mixed meal by type 2 diabetics

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The glycemic index concept neglects the insulin secretion factor and has not been systematically studied during mixed meals. Six starch-rich foods were tested alone and in an isoglucido-lipido-protidic meal in 18 NIDDs and compared with a glucose challenge. These test meals were randomly assigned using a three factor experiment design. All three tests contained 50 g carbohydrate; mixed meals were adjusted to bring the same amount of fat (20 g), protein (24 g), water (300 mL), and calories (475 kcal) but not the same amount of fiber. Whatever the tested meals, foods elicited a growing glycemic index hierarchy from beans to lentils, rice, spaghetti, potato, and bread (mean range: 0.21 +/- 0.12-92 +/- 0.12, p less than 0.001). Mixing the meals significantly increased the insulinemic indexes (p less than 0.05) and introduced a positive correlation between glycemic and insulinemic indexes (n = 6, r = 0.903; p less than 0.05). The glycemic index concept remains discriminating, even in the context of an iso-glucido-lipido-protidic meal. Insulinemic indexes do not improve discrimination between foods taken alone in type 2 diabetics: they only discriminate between foods during mixed meals, similarly to glycemic indexes.
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Insulinemic and glycemic indexes of six starch-rich
588 Am I Clin Nuir 1987;45:588-95. Printed in USA. C 1987 American Society for Clinical Nutrition
foods taken alone and in a mixed meal by
type 2 diabetics1-3
Francis RJ Bornet, MD, PhD, Dominique Costagliola, PhD, Saiwa W Rizkalla, MD, PhD,
Anne Blayo, MD, Anne-Marie Fontvieille, BS, RD. Marie-Jo#{235}lle Haardt, MD,
Martine Letanoux, MD, Georges Tchobroutsky, MD, and Gerard Slama, MD
ABSTRACT The glycemic index concept neglects the insulin secretion factor and has not been
systematically studied during mixed meals. Six starch-rich foods were tested alone and in an iso-
glucido-lipido-protidic meal in 18 NIDDs and compared with a glucose challenge. These test meals
were randomly assigned using a three factor experiment design. All three tests contained 50 g car-
bohydrate; mixed meals were adjusted to bring the same amount of fat (20 g), protein (24 g), water
(300 mL), and calories (475 kcal) but not the same amount of fiber. Whatever the tested meals, foods
elicited a growing glycemic index hierarchy from beans to lentils, rice, spaghetti, potato, and bread
(mean range: 0.21 ±0.12-92 ± 0.12, p <0.001). Mixing the meals significantly increased the insu-
linemic indexes (p <0.05) and introduced apositive correlation between glycemic and insulinemic
indexes (n = 6, r=0.903; p <0.05).
The glycemic index concept remains discriminating, even in the context of an iso-glucido-lipido-
protidicmeal. Insulinemic indexes do not improve discrimination between foods taken alone in type
2 diabetics: they only discriminate between foods during mixed meals, similarlyto glycemic indexes.
Am J C/in Nuir 1987;45:588-95.
KEY WORDS Glycemic index, insulinemic index, starch-rich foods, diabetes, mixed meals
Introduction
Emphasis has been put recently on a more
liberal use of carbohydrates in the diet of di-
abetic patients (1-4) so that this type of food
now represents 50-60% ofdaily caloric intake.
In the mid 70s, Crapo et a! published their
first studies on the postprandial plasma glucose
and insulin response to different carbohydrates
(5-8). This opened a new era of research on
metabolic effects of carbohydrate and led to
the introduction by Jenkins et al of the gly-
cemic index concept (9), in which effects of
such foods were classified in relationship to
the response elicited by a glucose challenge.
Even if this index was only intended to “pro-
vide physiological data on the blood glucose
response in man . . . to a range of foods . .
to supplement tables based solely on chemical
analysis” (9) and was, in this regard, real prog-
ress, these indexes could be regarded as insuf-
ficient. As recently pointed out by Coulston
et a! (10) “a major concern is that the main
focus of these studies has been the glycemic
response rather than evaluating both plasma
glucose andinsulin response.” The concept of
glycemic index indeed neglects the insulin se-
cretion factor, which might be ofmajor clinical
significance in the nondiabetic as well as in
the type 2 (noninsulin-treated) diabetic pop-
ulation since peripheral hyperinsulinism could
be a risk factor for atherosclerosis (1 1, 12).
Moreover, most of these studies have not
shown whether the concept ofglycemic index
persists when carbohydrate foods are incor-
porated in a mixed meal, which is the most
common manner in which they are consumed.
1From the Department of Diabetes, H#{244}tel-DieuHos-
pital, the Unite de Recherches Biomath#{233}matiques et Bio-
statistiques, Paris; and the Umt#{233}de Recherches Statis-
tiques, Villejuif, France.
2Supported by a grant from Pierre et Marie Curie Uni-
versity, Paris, France.
3Address reprint requests toG#{233}rardSlama, MD, Service
de Diab#{233}tologie, H#{244}tel-DieuHospital, 1, place du Parvis
Notre-Dame, 75181 Paris Cedex 04, France.
Received April 9, 1986.
Accepted for publication August 5, 1986.
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STARCH-RICH FOODS IN TYPE 2 DIABETICS 589
*RefTrivelli (1 3). Normal range 4-6%.
Coulston et a! (10) have suggested that any
study on the subject should meet five criteria:
1) development of any index of metabolic re-
sponse should include both glucose and in-
sulin; 2) response to various foods should be
studied in individuals for whom dietary rec-
ommendations are proposed; 3) studies should
be performed in the context ofa standardized
test meal containing representative portions
of fat, protein, and carbohydrate; 4) interpre-
tation of this research should be directed as
an aid to minimize the postprandial hypergly-
cemia in susceptible individuals; and 5) should
explore the chronic effects of any dietary
change. We shared this outlook when we
planned to explore the acute glycemic and in-
sulinemic effects of six common starch-rich
foods, ie bread, potato, rice, spaghetti, lentils,
and beans taken alone and as part of an iso-
g1ucido-1ipidoprotidic meal by type 2, non-
insulin-treated diabetic subjects.
Subjects and methods
Subjects
Eighteen type 2 diabetic subjects(6 women and 12 men)
participated as informed volunteers in this study, which
received approval from the Hospital Ethical Committee.
All had been diagnosed as type 2 diabetic subjects on the
basis of clinical history, a fasting blood glucose value
140 mg/100 mL, and a 2 h-postprandial value  190
mg/100 mL; all laboratoiy assays were repeated twice with
a 1 mo interval before the diagnosis was accepted. Six
subjects were treated by diet alone and 12 by oral anti-
diabetic drugs [glibenclamide (Hoechst, Puteaux, France)
and/or metformin (Aron-Medicia, Suresnes, France)J on
an outpatient basis: their clinical characteristics are given
in Table I. Therapy was kept constant for 4 mo before
the study and throughout testing.
Methods
Each subject consumed three test meals on 3 consec-
utive days: starch-rich food taken alone (meal A), the same
food in the same amount taken in the course of a meal
enriched in fat and protein (meal M), and the reference
glucose tolerance test (OGTT), taken as the third meal.
Six starch-rich foods were tested: white bread, spaghetti,
white rice, instant flaked potatoes, dried kidney beans,
and dried lentils. Each food was tested by three different
subjects. The type offood and the order ofthe three meals
(A, M, and OGT) were randomly assigned using a three
factor experiment design.
Constitution ofihe meals (Fig 1 and Table 2)
Every test meal contained 50 g available carbohydrate
(CHO) either as starch (meal A and meal M) or as glucose
(OG1’f). For meals A the fat (0.25-1.30 g/meal), protein
(4.2-20.5 g/meal), and caloric(219-290 kcal/meal) content
TABLE 1
Patient characteristics
No. Age Diabetes
duration Sex
M/F BMI Fasting pLasma
giucoic Fasting plasma
insulin HbA1
Antidiabetic drugs
Glibenclamide Metformin
yr yr kg/rn’ mg/I mL mUI/L % mg/day mg/day
I 68 9 M 27.7 123 31 8.2 12.5 -
2 47 15 F 40.5 186 23 7.7 7.5 1700
359 6 M 28.3 126 17 7.4 -1700
4 62 12 F 29.0 233 21 13.3 12.5 1700
556 1M30.5 142 29 9.1 12.5 1700
6 51 0.2 F 32.8 104 22 6.9 - -
7 32 0.2 M 32.7 151 16 9.0 .- 1700
8 62 20 M 21.8 84 7 6.5 7.5 1700
970 1 F 25.0 124 25 8.3 - -
10 59 11 M 27.5 126 20 8.2 - -
11 52 1 M 26.8 103 17 7.1 - -
12 60 13 M 22.1 198 10 10.3 12.5 850
13 52 9 M 21.0 102 16 6.6 --
14 65 25 F 30.7 114 11 8.3 --
15 62 19 M 21.9 111 18 8.2 12.5 -
16 61 2.5 M27.5 133 26 7.5 -1700
17 40 4 F 28.0 126 17 8.5 12.5 1700
18 73 10 M 27.7 163 19 7.9 12.5 -
Mean 57 8.8 12/6 27.9 136 19.2 8.3
SEM 2 1.8 1.1 9 1.5 0.5
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MEAL M
590 BORNET ET AL
(O,3g- I,3g)
(4,2g-20,5g)
CHO
50g
MEAL A
FIG 1. Analytical composition of meals in terms of
carbohydrate (CHO), fat, and protein.
depended on the nature of the food tested; 250 mL de-
caffeinated coffee was allowed for all meals A.
Meals M were made ofthe same amounts ofthe same
tested foods as meals A with the addition of butter and
Comt#{233}cheese in order to obtain constant CHO (50 g,
43%), fat (20 g, 37%), protein (24 g, 20%), energy (475
kcal), and total-water intake (300 mL). A variable quantity
of decaffeinated coffee was allowed in order to meet this
300 mL requirement, which ranged from zero for the po-
tato meal to 240 mL for the bread meal according to in-
trinsic water content of foods as described in reference
tables (14, 15). As indicated in Table 2, fiber content of
test meals differed from one starch-rich food to another
(meals A) and was not adjusted to be constant.
The OGT contained 50 g monodehydrated 1)-glucose
diluted in 250 mL water and was taken in a few seconds;
meals A and M were taken in 15 mm.
Test meals were taken at 0800 h after a 12 h fasting
period with patients taking their usual oral drugs when
indicated.
Processing of the foods
We used white bread (french baguette, industrially pro-
duced), long-grain white rice from Surinam, industrially
produced durum wheat flour spaghetti, dned kidney beans,
and green lentils. Rice and spaghetti were cooked in 2%
salted boiling water for 25 and 20 mm, respectively. Beans
were soaked 12 h in 2% salted water then cooked in this
water in a pressure cooker for 25 mm after rise in pressure.
Lentils were cooked without prior soaking in the same
manner for 40 mm. At the end of the cooking time no
residual water was left thus loss of solubiized starch was
avoided.
All foods were cooked in the same batch; each portion
was frozen at -20#{176}Cand thawed in a microwave oven
when needed.
Potato meals consisted of potato flakes extemporane-
ously prepared with 270 mL warm water.
Blood sampling
Blood samples were drawn 30 mm before and every
30 mm for 180 mm after the start of the meal. Samples
were immediately centrifuged and frozen at -20#{176}Cfor
later assay.
Plasma glucose was assayed using a glucose-oxidase
method (Beckman Autoanalyzer II, Beckman, Fullerton,
CA; intra-assay reproducibility is 2%). Plasma insulin was
tested by a radio immunoassay (Anti-insulin antibody,
Novo Industri, Copenhagen, Denmark) using a charcoal
separation (intra-assay reproducibility is 6%).
The mean glycemic index (0!), calculated for each pa-
tient and each test meal, is the ratio between the incre-
mental area under the 3 h glycemic-response curve to a
food and the incremental area under the 3 h glycemic-
response curve to glucose with the result multiplied by
100. The insulin index was calculated the same way from
the respective insulin curves.
Statistical analysis
We designed a three way experiment design (6 X 3 X 2)
with one case observation per cell. The analysis of variance
allowed us to test the effect of the following factors: type
of starch-rich food, type of meal (alone or mixed meal),
interaction between type of food and type of meal, and
subjects(nested within type offood factor). Multiple corn-
parisons were made using the method of Neuman and
Keuls (16). A possible association between glycemic and
insulin indexes was tested using a correlation analysis. Re-
suits are given as mean ±SEM.
Results
Figures 2a and 2b show the mean curves of
incremental plasma glucose and insulin van-
ations for each ofthe foods tested (meal A and
meal M) and the corresponding OGTT. The
comparisons between the results observed are
best characterized by glycemic and insulin in-
dexes.
Figure 3 shows the mean results of the 0!
for meal A and meal M. For meal A the GI
was very different from one type of food to
another with decreasing from bread (95
±15%) to potato (74 ± 12), spaghetti (64
±15), rice (56 ±2), lentils (30 ± 15), and beans
(23 ±1). For meal M GIs tended to be, on
average, 20% lower than for meal A and in
the same order. The differences between foods
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.ojjee
INGESTED CHO PROTEIN FAT CRUDE WATER ENERGY TOTAL MEAL
FOODS WEIGHT’ FIBER VALUE WATER ENERGY
TESTED (g) (g) (g) (g) (g) (g) (kcal) (g) (kcal)
*
WHITE
BREADt
Coffee
Chees4
Butte4
98 50
240 -
50 0.75
250
Coffee
i-i-- 250
32(1
(50)
8 1.17 0.88 37.50
- - - 240
15 15 -19.30
-3.32 -0.60
- - - 250
.4
POTA TOt
Coffee
Cheese
Butter
242
I98
30
a)
‘Ii
50 4.15 0.30 0.70 273.50
65 0.97 19.50 19.50 -2;.OO
250
Coffee 250
221)
RICEt (62)
Coffee Ito
Cheese 65
Butter -
219
257
Coffee
50 4.2 0.25 0.18 165
- - - - hO
0.97 19.50 19.50 -25.00
- - - - 250
219
257
SPAGHE1TIt
Coffee
Ctwese
Butter
Coffee
iii
25(1
210
(67)
130
so
250
2O)
(80)
1110
20
I5
50 8.6 0.8 0.20 150
- - - - 130
0.75 15 15 -19.25
- - 4.15 -0.78
- - - - 250
BEANSt
Coffr
Cheese
Butter
241
198
37.60
Coffee 250
iii-
50 17.3 1.3 3.25 130
- - - - 160
0.30 6 6 -7.20
- - 12.45 -2.30
- - - - 250
LENTIISt
Coffee
Cheec
Butter
280
79.20
112.80
225
(83)
14U
12
18
a)
a) ca
50 20.5 0.9 3.25 153 290
- - - - 140 -
0.18 3.60 3.60 -4.60 47.50
- - 14.90 -2.80 135.50
STARCH-RICH FOODS IN TYPE 2 DIABETICS
TABLE 2
Meal Composition
*The figures represent cooked foods. Dry weight is given in brackets.
tAccording to the Scientific Ciba Geigy Table (14).
tAccording to Ostrowsky’s Table (15).
591
were significant (p <0.001), but the differences
between meal A and meal M were not. mdi-
vidual comparisons showed that the bread GI
was significantly different from that of spagh-
etti (p <0.001), rice (p <0.01), lentils (p
<0.001), and beans (p <0.001). Potato 0!
was significantly different from that of lentils
(p <0.01) and beans (p <0.01); rice 0! was
significantly different from that of beans (p
<0.05).
Figure 4shows the mean insulinemic index
result for each food tested alone and during a
mixed meal. For meal A the insulin indexes
were almost identical, ‘95%, for every food
tested except bread (178 ± 3 1%). However,
the difference was not significant.
Mixing the meals significantly increased in-
sulin secretion (p <0.05) but also reintroduced
the hierarchy observed for the glycemic in-
dexes: insulin index for bread was 278 ± 18%;
for potato 274 ±164; for spaghetti 172 ±38;
forrice 126 ± 16; forlentils 103 ± 37; and for
beans69± 13.
Figure 5 shows the correlation between the
mean glycemic indexes and mean insulinemic
indexes of meals A and M: a significant car-
relation was only found for meal M (r =0.903,
p <0.05).
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150
0
0
I-
C)
E
w
(I)
0
C.)
0
(0
E
0.
C
0
E
C)
0
C
BEAD
SPA4
Time (minutes)
FIG 2A and 2B. Mean incremental plasma glucose and insulin variations observed during 180 mm following OGTT
(#{149}),meal A (food taken alone)(O), and meal M (mixed mea1)(i) in three type 2 diabetic patients for each food tested
(Fig 2a: bread, rice, and spaghetti; Fig 2b: potato, lentils, and beans). Mean ± SEM.
Time (minutes)
592 BORNET ET AL
iJ
 
C
#{149}inrJ
E_______
Discussion
Glycemic indexes have been proposed as a
way of classifying CHO containing foods ac-
cording to blood glucose responses. The
methodology used to calculate such indexes
has varied in many ways: the populations
tested have been normal and/or type 2 diabetic
subjects; they have been treated either by diet
alone, diet and oral antidiabetic agents, or by
insulin; the reference carbohydrate chosen also
has varied in terms of quality (glucose or white
FIG 3. Mean glycemic index calculated for the six foods
tested alone (meal A; black bars) and in a mixed meal
(meal M, white bars) in type 2 diabetic patients. n= 3;
mean ±SEM.
bread) (9, 17) and quantity (25 or 50 g) (9,
18). Moreover, and as pointed out by Coulston
et al (10), the concept of glycemic index ne-
glects the insulin secretion aspect and has not
yet been systematically studied in the usual
way in which foods are consumed, which is in
mixed meals. Even if they did not calculate
glycemic and insulinemic indexes, Coulston
et al (19, 20), while studying combinations of
j1jJ
FIG 4. Mean insulinemic index calculated for the six
foods tested alone (meal A; black bars) and in a mixed
meal (meal M, white bars) in type 2 diabetics. n= 3; mean
±SEM.
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MEAN GLYCEMUC INDEX (%)
STARCH-RICH FOODS IN TYPE 2 DIABETICS 593
><
w
z
Cu)
w
z
-J
U)
z
z
Ui
FIG 5. Correlation between the mean glycemic indexes
(x axis) and insulinemic indexes(y axis) observed for food
tested alone [meal A (#{149})jor in a mixed meal [meal M
(0)]. Each point represents the mean ±SEM ofthree test
meals. For meal A: r=0.675, NS; for meal B: r= 0.903,
p<0.05; y =3.05x +0.33; Br =bread, P = potato, R
=rice, S =spaghetti, Be =beans, and L =lentils.
foods, showed that substitution ofone starchy
food for another in the course ofa mixed meal
elicited different glucose responses in glucose-
intolerant subjects and different insulin re-
sponses in normal subjects. These results have
not been observed in type 2 diabetic subjects
(21-23). In these lafter studies the lack of in-
fluence may be due to the type ofmeals tested:
amixing ofCHO foods with different GIs but
with similar mean calculated GIs (22).
This study has attempted to calculate for
type 2 diabetic subjects the glycemic and in-
sulinemic indexes of CHOs taken alone and
during mixed meals.
Data concerning glycemic indexes
Our results are in good accordance with
those of other authors (9, 24, 25) who stressed
that foods that contain complex carbohydrates
as well as foods that contain simple sugars (26,
27) are, under acute conditions, sometimes
highly hyperglycemic; some foods may have
one fourth the glycemic effect of others. The
differences observed may be due to various
factors. For example it is well known that
mixing meals reduces glycemic responses (28,
29). We observed a trend towards a reduction
of 15-20%, which was not statistically sig-
nificant, even though fat and protein were at
levels of 37 and 20%, respectively. The ob-
served lack ofsignificant effect ofmixed meals
in our study might be due to the small number
of subjects tested for each food. In our study,
hierarchy of the GIs observed for foods taken
alone persists with our mixed meals, which
confirms the conclusions ofWolever et a! (30)
and demonstrates that the intrinsic nature of
the starchy food seems to be a predominant
factor. The differences observed cannot be due
to the fat, protein, and water content, as these
three factors were kept constant throughout
the mixed meals. When fat is added to a CHO
meal (31), it reduces glycemic response prob-
ably by slowing gastric emptying rather than
by modifying starch availability (32).
The GI differences observed may be par-
tially due to fiber content of the meals, as the
foods with the high-fiber content (beans, len-
tils; Table 2) have the lower 0!. However, de-
spite comparable fiber content, GIs may vary
considerably. The variable susceptibility of
starches or starchy foods to amylase has also
been shown in vitro (33-36). Furthermore,
starch legumes have a high-amylose content
(37), and the high-amylose content of starch
is a determinant factor ofsusceptibility to hy-
drolysis (31). Rice with high-amylose content
elicits a reduced glycemic response in corn-
parison with low-amylose rice (38).
In addition mechanical barriers such as
protein matrix (ie, gluten) that encapsulates
gelatinized starch granules limit access to am-
ylase and reduce the starch availability (39).
The high-gluten content of spaghetti may ex-
plain its lower 0! than that of another wheat
product such as bread.
Data concerning the insulinemic indexes
Our study has shown that, with the excep-
tion of bread, all the foods tested have ap-
proximately the same insulinogenic index
(-95%) when taken alone. In opposition with
our findings and in a comparable work on
foods ingested alone in impaired glucose to!-
erance patients (7) and in type 2diabetics (8),
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594 BORNET ET AL
Crapo et al showed that insulin secretion and,
therefore, insulinemic indexes vary from one
food to another in the same direction as the
glycemic response. In addition (as can be
extrapolated from their data), insulin indexes
were comparable in normal (6), impaired glu-
cose tolerance (7), and type 2diabetic (8) sub-
jects. The differences observed with our study
might be due to differences in severity of di-
abetes, endogenous insulin secretion capaci-
ties, and/or the use of oral drugs in our work.
The results observed in our study with bread
meals differ consistently from those of other
authors for glycemic response [95% vs 85% for
Crapo et a! (8) and 69% for Jenkins et al (9)]
and even more so for insulinemic response
[173% in our study vs ‘-85% for Crapo et a!
(8)]. These discrepancies may be due to dif-
ferences ofwheat origin and bread processing.
Whatever the cause, this invalidates the choice
of bread as an internationally accepted stan-
dard for 0! calculation. This is also true for
many other foods when discrepancies have
been found. Glucose, although not a real food,
is the best-known reference CHO.
During the mixed meals, insulin secretion
increased and the same hierarchy for insulin-
emic indexes as for GIs appeared with a pos-
itive correlation between both. The addition
ofproteins to CHOs potentiates insulin secre-
tion in both healthy (40) and diabetic subjects
(41, 42) (Fig 5). In mixed meals when protein
content is kept constant, the insulinemic index
correlates with the glycemic index (Fig 5).
Thus the synergistic effect of proteins and
CHOs is all the greater when the glycemic in-
dcx is high. This phenomenon seems to re-
main valid for some foods taken alone when
they are themselves rich in protein (lentils and
beans).
At this point we may ponder on the rele-
vance of an insulinemic index for choosing a
CHO food. As Coulston et a!, we postulated
that such indexes may have been of help in
classifying CHO foods. In fact our results show
that, at least in the population studied and with
the six foods tested, insulinemic indexes do
not seem to contribute any supplementary
data for classification: there is no goodfood in
terms of GI which becomes bad in terms of
insulinemic index (as far as a high insulinemic
index can be regarded as pejorative) and vice
versa. Studies on insulinemic indexes may
have some importance in the understanding
of physiological events in nutrition.
The conclusions of our study, which is one
of the first to compare simultaneously gly-
cemic and insulinemic indexes offoods taken
alone and in a mixed meal with constant CHO,
fat, protein, and water content, are the fol-
lowing:
1) The GI concept remains discriminating
even in the context of a mixed meal in type 2
diabetics, which validates the use of GI for
choosing foods even in mixed meals.
2) The insulinemic index does not bring
greater discrimination between CHO-foods
but remains ofmterest in physiological studies.
3) Bread should not be taken as the refer-
ence food for calculating 0! ifwe want to have
results that are ofgeneral significance. fl
We thank Mrs Josette Boillot, Mrs Nelly Desplanque,
Mrs Annie Chevalier, and Mrs Madeleine Bros for their
technical assistance and Mrs Annie Chevalier for her
graphical skill.
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by guest on July 11, 2011www.ajcn.orgDownloaded from
... In another study with healthy participants, a considerably larger serving of cooked green lentils (715 g) led to a 56.5% relative reduction in insulin Cmax at 60 min compared to the control [10]. In diabetic participants, 225 g of cooked green lentils containing 20.5 g of protein was associated with an insulin Cmax of 216 ± 66 pmol/L at 100 min [18], and similarly a 297 g serving of cooked lentils with 22.4 g of protein led to an insulin Cmax of 174 ± 6 pmol/L at 120 min [19]. These two studies required T2D participants to consume 50 g of carbohydrates in control treatments, and the relative reductions of insulin Cmax were calculated to be 24% after a 225 g serving [18] and 31% after a 297 g serving of lentils [19]. ...
... In diabetic participants, 225 g of cooked green lentils containing 20.5 g of protein was associated with an insulin Cmax of 216 ± 66 pmol/L at 100 min [18], and similarly a 297 g serving of cooked lentils with 22.4 g of protein led to an insulin Cmax of 174 ± 6 pmol/L at 120 min [19]. These two studies required T2D participants to consume 50 g of carbohydrates in control treatments, and the relative reductions of insulin Cmax were calculated to be 24% after a 225 g serving [18] and 31% after a 297 g serving of lentils [19]. The reduction of insulin Cmax appears to be independent of lentil serving, as reductions of similar magnitude were seen with both low and high lentil serving sizes. ...
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Pulse consumption has been shown to confer beneficial effects on blood glucose and insulin levels. Lentil consumption, in particular, consistently lowers acute blood glucose and insulin response when compared to starchy control foods. The mechanism by which lentils lower postprandial blood glucose response (PBGR) and insulin levels is unclear; however, evidence suggests that this effect may be linked to macronutrients and/or the amount of lentils consumed. This review attempts to consolidate existing studies that examined lentil consumption and glycemic and/or insulinemic responses and declared information on macronutrient composition and dietary fibre content of the foods tested. Collectively, these studies suggest that consumption of lentils reduces PBGR, with the minimum effective serving being ~110g cooked to reduce PBGR by 20%. Reductions in PBGR show modest-to-strong correlations with protein (45–57 g) and dietary fibre (22–30 g) content, but has weaker correlations with available carbohydrates. Increased lentil serving sizes were found to moderately influence relative reductions in peak blood glucose concentrations and lower the area under the blood glucose curve (BG AUC). However, no clear relationship was identified between serving and relative reductions in the BG AUC, making it challenging to characterize consistent serving–response effects.
... A number of randomized controlled trials have assessed the effect of pulse intake on acute post-prandial and longterm glucose response [14][15][16][17][18][19][20][21][22][23]. The studies differed in the type of pulses used, processing, doses and control group, and in different volunteer profiles [6,[24][25][26][27][28][29][30][31][32][33]. The study outcomes vary considerably with low quality of evidence and, therefore, the true effect size of pulse intake on measures of glycemic handling remains unclear [34]. ...
... In alignment with blood glucose, pulse intake favorably affected post-prandial insulin levels with a larger effect in T2D population where reduction in PPGR was greater. There were large variations between RCTs with regards to characteristics of participants such as mean age (22-66 y) and BMI (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31), that might influence insulin secretion and sensitivity. ...
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Purpose Findings from randomized controlled trials (RCTs) evaluating the effect of pulse intake on glycemic control are inconsistent and conclusive evidence is lacking. The aim of this study was to systematically review the impact of pulse consumption on post-prandial and long-term glycemic control in adults with and without type 2 diabetes (T2D). Methods Databases were searched for RCTs, reporting outcomes of post-prandial and long-term interventions with different pulse types on parameters of glycemic control in normoglycemic and T2D adults. Effect size (ES) was calculated using random effect model and meta-regression was conducted to assess the impact of various moderator variables such as pulse type, form, dose, and study duration on ES. Results From 3334 RCTs identified, 65 studies were eligible for inclusion involving 2102 individuals. In acute RCTs, pulse intake significantly reduced peak post-prandial glucose concentration in participants with T2D (ES – 2.90; 95%CI – 4.60, – 1.21; p ≤ 0.001; I ² = 93%) and without T2D (ES – 1.38; 95%CI – 1.78, – 0.99; p ≤ 0.001; I ² = 86%). Incorporating pulse consumption into long-term eating patterns significantly attenuated fasting glucose in normoglycemic adults (ES – 0.06; 95%CI – 0.12, 0.00; p ≤ 0.05; I ² = 30%). Whereas, in T2D participants, pulse intake significantly lowered fasting glucose (ES – 0.54; 95%CI – 0.83, – 0.24; p ≤ 0.001; I ² = 78%), glycated hemoglobin A1c (HbA 1c ) (ES – 0.17; 95%CI – 0.33, 0.00; p ≤ 0.05; I ² = 78) and homeostatic model assessment of insulin resistance (HOMA-IR) (ES – 0.47; 95%CI – 1.25, – 0.31; p ≤ 0.05; I ² = 79%). Conclusion Pulse consumption significantly reduced acute post-prandial glucose concentration > 1 mmol/L in normoglycemic adults and > 2.5 mmol/L in those with T2D, and improved a range of long-term glycemic control parameters in adults with and without T2D. PROSPERO registry number (CRD42019162322).
... There is evidence that postprandial glycemic and insulinemic responses to foods differ based on the amount and characteristics of the carbohydrate ingested [35][36][37]. As shown in the McKeown investigation above, the glycemic index (GI) can be used to study the degree to which glucose levels are affected by carbohydrate type [36] According to a randomized cross-over study in diabetic men by Rizkalla et al., fourweeks on a low glycemic diet produced lower postprandial glucose and insulin levels and areas under the curve than 4-weeks on a high GI diet [38]. ...
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The main goal of this investigation was to evaluate the relationships between several macronutrients and insulin resistance in 5665 non-diabetic U.S. adults. A secondary objective was to determine the extent to which the associations were influenced by multiple potential confounding variables. A cross-sectional design and 8 years of data from the 2011-2018 National Health and Nutrition Examination Survey (NHANES) were used to answer the research questions. Ten macronutrients were evaluated: total carbohydrate, starch, simple carbohydrate, dietary fiber, total protein, total fat, saturated, polyunsaturated, monounsaturated, and total unsaturated fat. The homeostatic model assessment (HOMA), based on fasting glucose and fasting insulin levels, was used to index insulin resistance. Age, sex, race, year of assessment, physical activity, cigarette smoking, alcohol use, and waist circumference were used as covariates. The relationships between total carbohydrate intake (F = 6.7, p = 0.0121), simple carbohydrate (F = 4.7, p = 0.0344) and HOMA-IR were linear and direct. The associations between fiber intake (F = 9.1, p = 0.0037), total protein (F = 4.4, p = 0.0393), total fat (F = 5.5, p = 0.0225), monounsaturated fat (F = 5.5, p = 0.0224), and total unsaturated fat (F = 6.5, p = 0.0132) were linear and inversely related to HOMA-IR, with 62 degrees of freedom. Starch, polyunsaturated fat, and saturated fat intakes were not related to HOMA-IR. In conclusion, in this nationally representative sample, several macronutrients were significant predictors of insulin resistance in U.S. adults.
... Protein is insulinogenic, that is, it stimulates production of insulin (Bornet, 1987), which lowers postprandial blood glucose by transporting glucose out of the bloodstream. Preloading with protein or high-protein beverages such as soymilk (Sun, Tan, Han, Leow, & Henry, 2017), soy protein isolate (Kashima et al., 2016), dairy milk (Sun et al., 2017), and whey protein and meat/cheese/fish (Tricò, Filice, Trifirò, & Natali, 2016) has been shown to increase insulin secretion prior to ingestion of the main carbohydrate meal, which lowered glycemic response compared to a blank (water) preload. ...
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Type 2 diabetes is increasingly prevalent in Asia, which can be attributed to a carbohydrate‐rich diet, consisting of foods in the form of grains, for example, rice, or a food product made from flours or isolated starch, for example, noodles. Carbohydrates become a health issue when they are digested and absorbed rapidly (high glycemic index), and more so when they are consumed in large quantities (high glycemic load). The principal strategies of glycemic control should thus aim to reduce the amount of carbohydrate available for digestion, reduce the rate of digestion of the food, reduce the rate of glucose absorption, and increase the rate of glucose removal from blood. From a food perspective, the composition and structure of the food can be modified to reduce the amount of carbohydrates or alter starch digestibility and glucose absorption rates via using different food ingredients and processing methods. From a human perspective, eating behavior and food choices surrounding a meal can also affect glycemic response. This review therefore identifies actionable strategies and opportunities across foods and meals that can be considered by food manufacturers or consumers. They are (a) using alternative ingredients, (b) adding functional ingredients, and (c) changing processing methods and parameters for foods, and optimizing (a) eating behavior, (b) preloading or co‐ingestion of other macronutrients, and (c) meal sequence and history. The effectiveness of a strategy would depend on consumer acceptance, compatibility of the strategy with an existing food product, and whether it is economically or technologically feasible. A combination of two or more strategies is recommended for greater effectiveness and flexibility.
... Thus, the GI calculation method suggested by Wolever and Jenkins [1], which reflects only carbohydrates, does not accurately account for the other nutrients included in the meal since the other macronutrients, such as fat and protein, may significantly influence the glycemic response to a mixed meal. Our data support those in other studies that observed differences between the calculated and actual GI measurements [10,[31][32][33]. ...
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BACKGROUND/OBJECTIVES The glycemic index (GI) is a measure of the postprandial glucose response (PPGR) to food items, and glycemic load (GL) is a measure of the PPGR to the diet. For those who need to maintain a healthy diet, it is beneficial to regulate appropriate levels of blood glucose. In reality, what influences the meal GI or GL depends on the macronutrient composition and the physical chemistry reactions in vivo. Thus, we investigated whether different macronutrients in a meal significantly affect the PPGR and the validity of calculated GI and GL values for mixed meals. SUBJECTS/METHODS 12 healthy subjects (6 male, 6 female) were recruited at a campus setting, and subjects consumed a total of 6 test meals one by one, each morning between 8:00 and 8:30 am after 12 h of fasting. PPGR was measured after each consumed meal and serial finger pricks were performed at indicated times. Test meals included 1) 68 g oral glucose, 2) 210 g rice, 3) rice plus 170 g egg white (RE), 4) rice plus 200 g bean sprouts (RS), 5) rice plus 10 g oil (RO), and 6) rice plus, egg white, bean sprouts, and oil (RESO). The incremental area under the curve (iAUC) was calculated to assess the PPGR. Mixed meal GI and GL values were calculated based on the nutrients the subjects consumed in each of the test meals. RESULTS The iAUC for all meals containing two macronutrients (RS, RO, or RE) were not significantly different from the rice iAUC, whereas, the RESO iAUC (2,237.5 ± 264.9) was significantly lower (P < 0.05). The RESO meal's calculated GI and GL values were different from the actual GI and GL values measured from the study subjects (P < 0.05). CONCLUSIONS The mixed meal containing three macronutrients (RESO) decreased the PPGR in healthy individuals, leading to significantly lower actual GI and GL values than those derived by nutrient-based calculations. Thus, consuming various macronutrient containing meals is beneficial in regulating PPGR.
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Eighteen (18) male albino rats were used to determine the glycemic indices of flour and starch component of two varieties, each, of maize and millet grains. The flour was obtained by milling the grains while the starch components were extracted from the grains using the method of Signh and Sadh (2009). Steeping, grinding, sieving and several rounds of centrifugation were carried out to obtain the starch component. The rats were administered with alloxan monohydrate (120mg/Kg body weight) to induce diabetes in them. After a twelve hour fast, fasting blood samples were collected by tail tipping and blood glucose analysed. The animals were then fed within fifteen minutes with test feed and further blood samples collected at 30, 60, 90 and 120 minutes from the commencement of feeding and analysed for blood glucose level using portable active accu-check glucometer. Two rats were fed with anhydrous glucose used as refernece feed. The area under the curve (AUC) for all the test and reference feeds were calculated by plotting the graph of blood glucose level in mg/dL against time in minutes. The glycemic indices were calculated by dividing the AUC of the test feed by that of the reference feed and multiplying by 100. The flour components of both the maize and the millet varieties gave lower glycemic index (maize sammez-11 flour gave G.
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Pulses are a major source for plant-based proteins, with over 173 countries producing and exporting over 50 million tons annually. Pulses provide many of the essential nutrients and vitamins for a balanced and healthy diet, hence are health beneficial. Pulses have been known to lower glycemic index (GI), as they elicit lower post prandial glycemic responses, and can prevent insulin resistance, Type 2 diabetes and associated complications. This study reviews the GI values (determined by in vivo methodology) reported in 48 articles during the year 1992–2018 for various pulse type preparations consumed by humans. The GI ranges (glucose and bread as a reference respectively) for each pulse type were: broad bean (40 ± 5 to 94 ± 4, 75 to 93), chickpea (5 ± 1 to 45 ± 1, 14 ± 3 to 96 ± 21), common bean (9 ± 1 to 75 ± 8, 18 ± 2 to 99 ± 11), cowpea (6 ± 1 to 56 ± 0.2, 38 ± 19 to 66 ± 7), lentil (10 ± 3 to 66 ± 6, 37 to 87 ± 6), mung bean (11 ± 2 to 90 ± 9, 28 ± 1 to 44 ± 6), peas (9 ± 2 to 57 ± 2, 45 ± 8 to 93 ± 9), pigeon peas (7 ± 1 to 54 ± 1, 31 ± 4), and mixed pulses (35 ± 5 to 66 ± 23, 69 ± 42 to 98 ± 29). It was found that the method of preparation, processing and heat applications tended to affect the GI of pulses. In addition, removal of the hull, blending, grinding, milling and pureeing, reduced particle size, contributed to an increased surface area and exposure of starch granules to the amylolytic enzymes. This was subsequently associated with rapid digestion and absorption of pulse carbohydrates, resulting in a higher GI. High or increased heat applications to pulses were associated with extensive starch gelatinization, also leading to a higher GI. The type of reference food used (glucose or white bread) and the other nutrients present in the meal also affected the GI.
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Blood glucose response and glycemic Index (GI) for broad bean kernels, whole broad bean, chickpea, lentils and kidney bean in normal human subjects using glucose as standard were studied. Twenty one male normal human subjects (age 22-35 years and body mass index, BMI 22-26 kg/m²) were chosen as volunteers for this study. The subjects were divided randomly into groups where each three or four of the subjects could submit to the experiments. After 12 hours overnight fast each subject was tested for blood glucose at 0 time before given the test food or glucose standard in an amount to provide 50 g carbohydrate. Glycemic response, Incremental Area under the Curve (IAUC) and Glycemic Index (GI) were determined and calculated. The results show that there were significant (P
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Blood glucose response and glycemic Index (GI) for whole wheat loaf, white flour loaf and white flour bread "Tannour bread" and wheat products such as burghul, habbiyah, reshta and spaghetti in normal human subjects using glucose as standard were studied. Twenty one normal male human subjects (age 22-35 years and body mass index, BMI 22-26 kg/m²) were chosen as volunteers for this study. The subjects were divided randomly into seven groups where each three of the subjects could submit to the experiments. After 12 hours overnight fast, each subject was tested for blood glucose at zero time before given the test food or glucose standard in an amount to provide 50 g carbohydrate. Glycemic response, Incremental Area Under the Curve (IAUC) and Glycemic index (GI) were determined and calculated. The results showed that there were no significant (P
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In an attempt to understand the mechanism for the extremely slow rate of digestion and absorption of carbohydrate from legumes, we have examined a number of factors which could potentially affect the process in vitro. The rate of hydrolysis of legume starch in vitro was not affected by the presence of fat (as either butter or an emulsion). However, it was significantly increased in commercially available canned bean preparations, suggesting that the high temperatures used in the canning process may alter the availability of starch in legumes. In vitro starch hydrolysis rate was also significantly increased by grinding legumes finely prior to cooking. Finally, the slow rate of digestion and absorption of legume carbohydrate does not appear to be due to viscosity since a) increasing the shaking rate of viscous mixture of either red kidney beans or lentils from 0 to 120 oscillations per minute did not affect the hydrolysis rate, and b) a thick viscous mixture of either of these legumes did not retard the diffusion of free glucose from a dialysis sac into the dialysate.
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Article
After accurate determination of the content of available carbohydrate in a wide variety of cereals, as in vitro method was used to study factors that influence hydrolysis rates of starch in foods. Fiber, physical form, cooking, and the possible presence of a natural amylase inhibitor were all shown to affect hydrolysis rates of starch. Fiber only exerted an inhibiting effect on the rate of hydrolysis when it formed a physical barrier to limit access of the hydrolytic enzymes to the starch (as in whole brown rice, for example). Particle size played an important role in determining the rate of hydrolysis. Cooking made the starch much more readily available for enzymic hydrolysis presumably by gelatinizing it. Stoneground wholemeal flour was hydrolyzed more slowly than white flour. This is consistent with the presence of a natural amylase inhibitor that has been isolated from wheat germ in the whole grain. Our results suggest that such amylase inhibitor activity is destroyed by passage through the roller mill, since the starch in wheat germ and standard wholemeal flour (i.e., not stoneground but reconstituted after passage through the roller mill) was hydrolyzed at a rate identical to white flour.
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We have studied the effects of dextrose, rice, potato, corn, and bread on postprandial plasma glucose and insulin responses in 16 subjects. All carbohydrate loads were calculated to contain 50 gm. of glucose. The data demonstrate (1) that dextrose and potato elicited similar plasma glucose responses whereas rice, corn, and bread elicited lower responses; (2) similarly, dextrose and potato elicited similar and greater plasma insulin responses than rice and corn, with the response to bread being intermediate; (3) when the study group was divided in half, on the basis of each subject's one-hour plasma glucose response to dextrose, the differences in the plasma glucose and insulin responses were greater in the subjects with the highest glucose response to dextrose than in the low responders. In conclusion, there is a range of plasma-glucose and insulin responses to different complex carbohydrates, with rice and corn producing the lowest response curves. Furthermore, these differences are accentuated in patients with reduced glucose tolerance.
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We have studied the effects of glucose, sucrose, and various starches on postprandial plasma glucose and insulin responses in 19 subjects. All carbohydrate loads were calculated to contain 50 gm. of glucose, and the response to each carbohydrate was tested twice: when given alone in a drink or when given in combination with other nutrients as a meal. The data demonstrate: (1) Glucose and sucrose elicited similar plasma glucose response curves, but sucrose elicited a somewhat greater (20 per cent) plasma insulin response. (2) Raw starch ingestion resulted in a 44 per cent lower glucose response and a 35-65 per cent lower insulin response than did either glucose or sucrose ingestion. (3) When carbohydrate was given as a meal the plasma glucose responses were 40-60 per cent lower than when the same carbohydrate was given as a drink, while the insulin responses were generally similar, and (4) when different cooked starches were compared, the plasma glucose and insulin responses to rice were significantly lower (50 per cent) than to potato. In conclusion, the size of the carbohydrate molecule appears to influence the postprandial glucose and insulin responses such that more complex carbohydrates (starches) elicit lower responses. This effect may be related to differences in digestion rather than to differences in absorption.
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Exclusion of simple sugars from the diabetic diet is not always followed by patients and may not even be as crucial as was hitherto thought. We tested three types of mixed breakfasts (400 kcal, 50 g HCO) including an isoglucidic amount either of white bread (30 g), honey (20 g) or sucrose (15 g), at the critical morning period i.e. for breakfast, in a group of 21 Type 2 (non-insulin-dependent) diabetic patients (6 well- and 15 badly controlled). Mean plasma glucose and insulin levels were comparable on the three occasions: respectively with bread, sucrose and honey, peak glucose values were 18 mmol/l, 17.7 mmol/l and 17.5 mmol/l in the uncontrolled group versus 13.9 mmol/l, 12.8 mmol/l and 12.7 mmol/l in the well-controlled group. Peak insulin values were 33.6 mU/1,34.0 mU/l and 36.3 mU/l (p greater than 0.05) in uncontrolled patients against 57.5 mU/l, 54.8 mU/l and 52.5 mU/l in well-controlled subjects (p greater than 0.05). The mean increment in peak plasma glucose values for the three breakfasts was as follows: 6.9 mmol/l, 6.3 mmol/l and 6.2 mmol/l for the uncontrolled group against 7.2 mmol/l, 5.9 mmol/l and 6.2 mmol/l in well-controlled subjects; the mean increment in peak plasma insulin levels was 21.8 mU/l,22.0 mU/l and 24.2 mU/l in the controlled group versus 38.2 mU/l, 32.0 mU/l and 34.7 mU/l in the well-controlled subjects, all values being non-significantly different (p greater than 0.05). We conclude that, in acute conditions, simple sugars have no additional hyperglycaemic effect over an isoglucidic amount of bread in well-and in badly controlled Type 2 diabetic patients, even at breakfast.