ArticlePDF Available

Glycemic Response to Pasta: Effect of Surface Area, Degree of Cooking, and Protein Enrichment

  • INQUIS Clinical Research, Ltd

Abstract and Figures

To see whether food form, the degree of cooking, or protein enrichment affected the glycemic response to pasta, we gave test-meal breakfasts to 13 diabetic patients. Macaroni had a significantly greater glycemic index (GI) (68 +/- 8) than spaghetti (45 +/- 6, P less than .01); the GI of star pastina was intermediate (54 +/- 6). The GI of spaghetti was not significantly affected by cooking for 5 or 15 min (45 +/- 6 and 46 +/- 5, respectively), or by protein enrichment (38 +/- 4). The GI of spaghetti was similar in 11 non-insulin-dependent and 6 insulin-dependent diabetic patients (49 +/- 7 compared with 57 +/- 8). We conclude that different types of pasta may produce different glycemic responses but that these are not necessarily related to differences in cooking or surface area.
Content may be subject to copyright.
Response to Pasta: Effect of
Area, Degree of Cooking, and
M. S.
D. J. A.
L. U.
G. S.
R. G.
see whether food form, the degree of
or protein enrichment affected the glycemic response
pasta, we gave test-meal breakfasts to 13 diabetic patients. Macaroni had a significantly greater
index (GI) (68 ± 8) than spaghetti (45 ± 6, P < .01); the GI of star pastina was intermediate
± 6). The GI of spaghetti was not significantly affected by cooking for 5 or 15 min (45 ± 6 and
± 5, respectively), or by protein enrichment (38 ± 4). The GI of spaghetti was similar in 11 non-
and 6 insulin-dependent diabetic patients (49 ± 7 compared with 57 ± 8). We
that different types of pasta may produce different glycemic responses but that these are not
related to differences in cooking or surface area,
here is much interest in the glycemic responses to
carbohydrate foods in diabetic patients.1"8 Many
factors affect the glycemic response to meals: im-
portant among these are the food form,69 particle
protein content of the meal,12 and the degree of
cooking.713 Pasta is a popular starchy food, which, in the
form of spaghetti, has been shown to have a low glycemic
response, ~60% that of bread.4'6 However, there are many
different types of pasta available, and each type may have
potentially different metabolic effects because of different sur-
face areas and protein contents. In addition, the traditional
Italian method of cooking pasta
different from that in North
America; the Italians prefer their pasta relatively under-
cooked, or "al dente." We have therefore investigated the
effect on the glycemic response in diabetic patients of five
different forms of pasta: "overcooked" spaghetti, "under-
cooked" spaghetti, macaroni, star pastina (small pasta stars),
and protein-enriched spaghetti.
Seventeen diabetic patients who had no evidence of gastro-
paresis were studied. Six were classified as insulin dependent
(IDDM: fasting C peptide <0.6 ng/ml with no postprandial
rise) and 11 as non-insulin dependent (NIDDM). Charac-
teristics of the IDDM and NIDDM subjects, respectively,
age, 50 ± 6 and 70 ±
yr; ideal body weight,
107 ± 5 and 129 ± 10%; duration of diabetes, 19 ± 4 and
10 ± 2 yr; mean fasting blood glucose, 8.7 ± 0.8 and
7.2 ± 0.5 mmol/L; and mean HbAlc, 8.2 ± 0.7 and
6.7 ± 0.4%. The IDDM patients took a mean of
± 5 U
of insulin per day. Seven of the NIDDM patients were treated
with insulin (mean 28 ± 11 U/day), three with oral hypo-
glycemic agents, and one with diet only. Fasting fingerprick
capillary blood samples were obtained (Autolet lancets, Owen
Mumford, Woodstock, Oxford, UK). Five minutes after the
patients took their usual insulin or other diabetes medications
(if any), a test meal was given. Fingerprick blood samples
were obtained every 30 min for 3 h after the start of each
meal for measurement of whole blood glucose with an au-
tomatic analyzer (Model 27, Yellow Springs Instruments, Yel-
low Springs, OH).
Test-meal constituents and cooking methods
are shown in Table 1. White flour and the uncooked spaghetti
were analyzed for nutrient14 and dietary fiber content.15 Mac-
aroni and star pastina were assumed to have the same com-
position as the spaghetti from the same manufacturer.
Each test meal contained 50 g available carbohydrate from
white bread or pasta. For palatability, all the test meals were
served with 32 g cheddar cheese plus 100 g cooked tomato.
Each subject also took at least three test meals of white bread
only (mean 5.6 ± 0.7, range 3-12).
The test meals were consumed in random order. Four of
the IDDM subjects were unable to complete all the tests and
were given only the 5-min spaghetti test meal. Because no
significant difference in the glycemic response to spaghetti
Test-meal constituents, cooking methods, and mean ± SE glycemic indices
FoodDry wt
(g)Cooking methodGlycemic index'
White bread
White bread + C + Tt
5-min spaghetti§ + C + T
15-min spaghetti§ + C + T
Macaroni§ + C + T
Star pastina§ + C + T
Spaghetti|| + C + T
Baked in diet kitchen
Baked in diet kitchen
Boiled, 180 ml water, 5 min
Boiled, 300 ml water, 15 min;
frozen, reheated before test
Boiled, 180 ml water, 5 min
Boiled, 180 ml water, 5 min
Boiled, 180 ml water, 7 min
99 ± 4A
45 ± 6B
46 ± 6B
64 ± 8C
54 ± 6BC
Excess water from cooking was consumed with the test meals.
'Means not sharing the same letter superscript are significantly different (P < .01).
tWeight of flour. $C = 32 g mild cheddar cheese, T = 100 g cooked tomato. §Lancia-Bravo Foods Ltd., Toronto, Canada; ||Catelli Plus, Catelli Ltd.,
Montreal, Canada.
was found between IDDM and NIDDM subjects, the results
of 2 IDDM subjects who completed all the test meals were
pooled with the results of the 11 NIDDM subjects.
The study was approved by the human experimentation
ethics committee of the University of Toronto.
The glycemic indices (GIs) of the test meals
were calculated using the three white bread tests taken closest
to the date of each test meal, which has been shown to
minimize the variability of GI calculations.16 Results are ex-
pressed as means ± SE. The dimensions of uncooked pasta
were measured and the surface areas calculated by appropriate
formulas. Incremental areas under the blood glucose response
-O White bread
White bread plus cheese and tomato
60 120
Time (min)180
FIG. I. Mean ± SE blood glucose responses of 13 diabetic patients
(I I NIDDM and 2 IDDM) who took meals of white bread containing
50 g available carbohydrate or same amount of white bread with 32 g
cheddar cheese plus 100 g cooked tomato.
curves were calculated geometrically using the blood glucose
increments.17 Any area beneath the fasting blood glucose
level was ignored. After demonstration of significant heter-
ogeneity by two-way analysis of variance,18 the means were
compared using Fisher's test (if F > 10) or Tukey's test (if
F < 10). The significance of the differences for the six IDDM
patients who tested spaghetti and bread was assessed by Stu-
dent's t test for paired data.
Effect of added cheese and tomato. The effect on the glycemic
response of adding cheese and tomato to bread was negligible
(Fig. 1, Table 1). Therefore, no adjustment for added cheese
and tomato was made to the GI of the pasta meals.
of pasta. The mean glycemic responses to the
pasta meals were significantly lower than those to the bread
meals (Fig. 2, Table 1). The GI of spaghetti was significantly
lower than that of macaroni (P < .01). The glycemic re-
sponses to star pastina and protein-enriched spaghetti were
not significantly different from those to regular spaghetti (Fig.
Table 1).
Effect of
Cooking spaghetti for 5 or 15 min had
virtually no impact on the mean glycemic response (Fig. 2)
orGI (Table 1).
Comparison of IDDM with NIDDM subjects. IDDM and
NIDDM subjects responded similarly to spaghetti cooked for
5 min, or "al dente" (Fig. 3). The GIs for spaghetti in IDDM
(57 ± 8, N = 6) and NIDDM (49 ± 7, N = 11) patients
were not significantly different.
he results confirm the low GI of pasta.6 In addition,
they indicate that differences exist in the glycemic
response to different forms of pasta; these differ-
ences are not necessarily related to the surface area,
the degree of cooking, or protein enrichment.
Surface area has been ascribed an important role in deter-
O-White Bread
-A- Star Pastina
-O White Bread -#- Enriched Spaghetti
Spaghetti -Q- 15min Spaghetti
60 120
Time (min)180
± SE
blood glucose responses
of 13
diabetic patients
(11 N/DDM and
IDDM) who took meals of white
star pastina,
15-min {overcooked) spaghetti,
or protein-enriched
For each time, means not sharing same letter
are significantly different
(P < .01).
mining the rate of digestion of starch, which, in turn, is
related to the glycemic response.19 Increasing the surface area
by grinding rice, for example, has been associated with an
increased rate of enzymatic hydrolysis and enhanced glycemic
and endocrine responses.1011 Although the surface areas of
the 66-g portions of uncooked spaghetti, macaroni, and star
pastina were quite different
and 0.14 m2, re-
spectively), they did not relate to the glycemic response. This
was probably because the surface areas would have been in-
creased to different extents by chewing.
Starch produces a greater glycemic response when cooked
than when consumed raw,713 presumably because cooking
increases the degree of starch gelatinization and its suscep-
tibility to enzymatic digestion. In this study, the difference
between cooking spaghetti for 5 or 15 min was not reflected
in a different glycemic response, despite the fact that the
"overcooked" spaghetti was frozen and reheated as well as
cooked for a longer period of time and was, therefore, much
softer than the 5-min spaghetti. Nevertheless, our "over-
cooked" spaghetti may not have been overcooked enough to
see the true effect of the very-overcooked pasta that is some-
times eaten in North America (e.g., canned pasta).
The addition of protein to meals has been found to increase
insulin secretion and reduce the glycemic response12 only
when relatively large amounts of protein (30-50 g protein/
carbohydrate) are used. The lack of effect of added cheese
and tomato (containing 9.1 g protein) or of protein-enriched
spaghetti (2.3 g more protein than the portion of regular
o-White bread
-•- Aldente spaghetti
60 120
Time (min)180
SE blood glucose responses of 6 IDDM subjects (top
panel) and JJ NIDDM
(bottom panel) who took meab of
white bread or "al dente" spaghetti.
spaghetti) on the glycemic response was probably due to the
small amounts of protein added.
We conclude that pasta has a significantly lower GI than
bread. However, different types of pasta have different gly-
cemic responses that are not necessarily related to surface
area, the degree of cooking, or protein enrichment.
T.M.S. W. received a Fellowship from the
Kellogg's Company, Battle Creek, Michigan. These studies
were supported by the Natural Sciences and Engineering Re-
search Council of Canada.
From the Department of Nutritional Sciences, Faculty of Med-
icine (T.M.S.W., D.J.A.J., J.K., C.G., S.G., A.L.J., L.U.I.); and
the Division of Endocrinology and Metabolism, St. Michael's Hos-
pital (T.M.S.W., D.J.A.J., G.S.W., R.G.J.), University of Toronto,
Toronto, Ontario M5S 1A8, Canada.
Address reprint requests to Dr. Thomas M. S. Wolever at the
above address.
1 Otto, H., and Niklas, L: Differences d'action sur la glycemie
d'aliments contenant des hydrates de carbone. Consequences pur le
traitment dietetique du diabete sucre. Med. Hyg. 1980; 38:3424-
2 Crapo, P. A., Insel, J., Sperling, M., and Kolterman, O. G.:
Comparison of serum glucose, insulin, and glucagon responses to
different types of complex carbohydrate in noninsulin-dependent
diabetic patients. Am. J. Clin. Nutr. 1981; 34:184-90.
3 Bantle, J. P., Laine, D. C, Castle, G. W., Thomas, J. W.,
B. J., and Goetz, F. C: Post-prandial glucose and insulin
responses to meals containing different carbohydrates in normal and
diabetic subjects. N. Engl. J. Med. 1983; 309:7-12.
4 Jenkins, D. J. A., Wolever, T. M. S., Jenkins, A. L., Thome,
M. J., Lee, R., Kalmusky, J., Reichert, R., and Wong, G. S.: The
glycaemic index of foods tested in diabetic patients: a new basis for
carbohydrate exchange favouring the use of legumes. Diabetologia
5 Nuttall, F. Q., Mooradian, A. D., DeMarais, R., and Parker,
S.: The glycemic effect of different meals approximately isocaloric
and similar in protein, carbohydrate, and fat content as calculated
using the ADA exchange lists. Diabetes Care 1983; 6:432-35.
6 Jenkins, D. J. A., Wolever, T M. S., Jenkins, A. L, Lee, R.,
Wong, G. S., and Josse, R.: Glycemic response to wheat products:
reduced response to pasta but no effect of fiber. Diabetes Care 1983;
7 Vaaler, S., Hanssen, K. E., and Aagenaes, O.: The effect of
cooking upon the blood glucose response to ingested carrots and
potatoes. Diabetes Care 1984; 7:221-23.
8 American Diabetes Association: Policy Statement: Glycemic
effects of carbohydrates. Diabetes Care 1984; 7:607-608.
9 Haber, G. B., Heaton, K. W, Murphy, D., and Burroughs,
L. F.: Depletion and disruption of dietary fibre: effects on satiety,
plasma-glucose, and insulin. Lancet 1977; 2:679-82.
10O'Dea, K., Nestel, P. J., and
L: Physical factors
influencing postprandial glucose and insulin responses to starch.
Am. J. Clin. Nutr. 1980; 33:760-65.
11 Collier, G., and O'Dea, K.: Effect of physical form of carbo-
hydrate on the postprandial glucose, insulin, and gastric inhibitory
polypeptide responses in type 2 diabetes. Am. J. Clin. Nutr. 1982;
12 Nuttall, F. Q., Mooradian, A. D., Gannon, M. C, Billington,
C., and Krezowski, P.: Effect of protein ingestion on the glucose
and insulin response to a standardized oral glucose load. Diabetes
Care 1984; 7:465-70.
13 Collings, P., Williams, C, and MacDonald, I.: Effect of cook-
ing on serum glucose and insulin responses to starch. Br. Med. J.
14 AOAC Official Methods of Analysis, Washington, DC, As-
sociation of Official Analytical Chemists, 1980.
15 Protsky, L, Asp, N.-G., Furda, I., DeVries, J. W., Schweizer,
T. F, and Harland, B. F: Determination of dietary fiber in food
products, collaborative study. In Annu. Int. Meet. Assoc. Official
Agricultural Chemists, 98th, Washington, DC, October 29-No-
vember 2, 1984.
16 Wolever, T M. S., Nuttall, F. Q., Jenkins, D. J. A., Lee, R.,
Wong, G. S., Josse, R. G., andCsima, A.: Prediction of the relative
blood glucose response of mixed meals using the white bread gly-
cemic index. Diabetes Care 1985; 8:418-28.
17 Wolever, T. M. S., and Jenkins, D. J. A.: The use of the
glycemic index in predicting the blood glucose response to mixed
In press. Am. J. Clin. Nutr.
18 Snedecor, G. W., and Cochran, W. G.: Statistical Methods,
7th ed. Ames, Iowa State Univ. Press, 1980.
19 Jenkins, D. J. A., Wolever, T M. S., Thome, M. J., Jenkins,
A. L., Wong, G. S., Josse, R. G., and Csima, A.: The relationship
between glycemic response, digestibility, and factors influencing the
dietary habits of diabetics. Am. J. Clin. Nutr. 1984; 40:1175-91.
... As a consequence of this technological process, the microstructure of pasta is compact and relatively dense, limiting the hydrolysis of internal starch granules, which explains its richness in slow digestible starch and its reduced enzymatic susceptibility during digestion [9,12]. Postprandial studies conducted in both healthy and diabetic volunteers confirmed that durum wheat pasta induced a lower postprandial glucose response than other wheat-based products (i.e., bread) by virtue of its compact dense physical structure (dried pasta) and the network of gluten surrounding the starch granules [13][14][15][16]. On granules [13][14][15][16]. ...
... Postprandial studies conducted in both healthy and diabetic volunteers confirmed that durum wheat pasta induced a lower postprandial glucose response than other wheat-based products (i.e., bread) by virtue of its compact dense physical structure (dried pasta) and the network of gluten surrounding the starch granules [13][14][15][16]. On granules [13][14][15][16]. On the other hand, refined wheat pasta is significantly lower in fiber and micronutrients (i.e., minerals and vitamins) with respect to whole grain pasta [9], and it is well known that the biological value of wheat proteins is low due to the deficiency in some essential amino acids, such as lysine and threonine [17]. ...
... Dietary fiber, hydrocolloids, resistant starches and proteins have been shown to be able to slow the carbohydrate digestion rate [99,100]. Especially for other cereal-based items (category n 3), for GF pp (category n 5), and for those containing legumes (category n 6), GI values belonging to the same category were highly variable, reflecting the putative role of food properties [29,31,101], technological processing methods [14,15,20,54,102] and cooking time [12,31,103,104] in affecting carbohydrate bioavailability for pp, which could appear similar. Furthermore, since GI data for similar pp were presented as mean values and were collected from different human studies, the possible inter-individual variability in carbohydrate metabolism should also be taken into account [31,101,105,106]. ...
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 GI of spaghetti pastas may be affected by many factors, including the addition of soluble fiber, resistant starch, fats, proteins, processing, preparation and cooking methods, the physical form of the food, the type of sugars and starch, the ripeness or the maturity of the raw ingredients, etc. [6,18,23,24,45,46]. It has been shown that different brands of the same type of pasta, i.e., shape or size of circumference, may look and taste almost the same, but differences in the type of flour used, the technological aspects (time/temperature/humidity drying cycles; extrusion dies) and the cooking time can result in differences in the degree of starch gelatinization and consequently the GI values [18,26]. ...
... All three had significantly lower incremental glucose responses, glucose excursions, and GIs as compared to the reference foods. Our results are in agreement with others reporting that spaghetti produces a significantly smaller rise in blood glucose compared to breads (whole grain or white) or other types of pasta, indicating that differences in food form, independent of fiber content, may have marked effects on postprandial glycemia [18,24,26,45,[47][48][49]. Our results also agree with the International Food Tables [50,51]. ...
Full-text available
This randomized, single blind, cross-over study investigated the glycemic responses to three spaghetti No 7 types differing in dietary protein and soluble fiber content. Fourteen clinically and metabolically healthy, fasting individuals (25 � 1 years; ten women; BMI 23 � 1 kg/m2) received isoglucidic test meals (50 g available carbohydrate) and 50 g glucose reference, in random order. GI was calculated using the FAO/WHO method. Capillary blood glucose and salivary insulin samples were collected at 0, 15, 30, 45, 60, and 120 min. Subjective appetite ratings (hunger, fullness, and desire to eat) were assessed by visual analogue scales (VAS, 100 mm) at baseline and 120 min. All three spaghetti types (regular, whole wheat, and high soluble fiber–low carbohydrates) provided low GI values (33, 38, and 41, respectively, on glucose scale) and lower peak glucose values compared to glucose or white bread. No differences were observed between spaghetti No 7 types for fasting glucose, fasting and post-test-meal insulin concentrations, blood pressure (systolic and diastolic), and subjective appetite. Conclusions: all spaghetti No 7 types, regardless of soluble fiber and/or protein content, attenuated postprandial glycemic response, which may offer advantages to glycemic control.
... Moreover, cooking may also influence the glycemic response [31]. After the ingestion of raw starch, in fact, smaller responses of plasma glucose and insulin levels occur compared to the ingestion of cooked starch. ...
... Finally, the digestibility of a food may also be influenced by its form [33]. For instance, different forms of pasta show different GIs: macaroni pasta has a greater GI than spaghetti pasta [31]. ...
Full-text available
Type 2 diabetes mellitus has a high prevalence worldwide, with a rapidly increasing incidence even in youth. Nutrition, dietary macronutrient composition, and in particular dietary carbohydrates play a major role in the development of type 2 diabetes. The aim of this narrative review is to discuss the current evidence on the role of dietary carbohydrates in the prevention and management of type 2 diabetes. The digestibility or availability of carbohydrates and their glycemic index (and glycemic load) markedly influence the glycemic response. High consumption of dietary fiber is beneficial for management of type 2 diabetes, whereas high consumption of both glycemic starch and sugars may have a harmful effect on glucose metabolism, thereby increasing the risk of developing type 2 diabetes in the presence of genetic predisposition or making its glycemic control more difficult to achieve in people with established T2D. Therefore, the same dietary macronutrient may have harmful or beneficial effects on type 2 diabetes mainly depending on the subtypes consumed. Some other factors are involved in glucose metabolism, such as meal composition, gut microbiota and genetics. For this reason, the glycemic response after carbohydrate consumption is not easy to predict in the single individual. Nutrition suggested to subjects with known type 2 diabetes should be always person-centered, considering the individual features of each subject.
... Indeed, the higher surface to weight ratio of vermicelli compared to spaghetti may explain the higher accessibility of α-amylase to starch [31]. Similarly, Wolever et al. found that macaroni produced a significantly higher in vivo glucose response than spaghetti in diabetic subjects [32]. ...
Full-text available
The rate at which starch is digested in the human intestine elicits different glycemic responses and reflects the glycemic index (GI) of foods. In vitro measurement of starch digestibility can reflect the GI of food. Differences in starch digestibility among four durum wheat pasta samples, couscous, and bread were evaluated to better describe the role of the pasta making process in affecting starch digestibility. Statistical differences in RDS (rapidly digestible starch), SDS (slowly digestible starch), and RS (resistant starch) of products were found (p < 0.05). As expected, pasta samples showed the highest value of SDS/av starch compared to couscous and bread. Fusilli and cavatelli samples presented the highest SDS/av starch ratio (55.80 ± 3.06% and 53.91 ± 3.50%, respectively), then came spaghetti 49.39 ± 2.83% and penne 45.93 ± 1.19%, while couscous presented the lowest value of SDS/av starch (2.64 ± 0.50%), followed by bread (11.78 ± 2.63%). Our study confirmed that the pasta making process efficiently mediates an increase in SDS/Av starch content, which has been specifically quantified above 40%, therefore strongly related to a lowered glycemic response in vivo. Our results strengthened the concept that pasta is a good source of SDS, which makes it useful for glycemic control.
... Still, incorporation at a higher percentage can reduce the G.I. The figure 1 results justified the concept with the protein-incorporated pasta study (Wolever et al. 1986). ...
Full-text available
Diabetes has become a potential epidemic in India, affecting millions of individuals. The present study attempted to develop a low glycaemic index simulated wheat flour as a solution for diabetic patients. The simulated wheat flour was formulated from modified rice flour, gluten protein, and extracted okra gum. The glycaemic index was determined using the in vitro digestibility method. The glycaemic index of the Indian flatbread prepared using the final formulation of simulated wheat flour was 12.05 ± 0.73, which is 71.4 % less than the whole wheat Indian flatbread (42.14 ± 2.44). The protein and energy content of the simulated wheat flour was higher than that of the whole wheat flour. The SEM images and DSC thermogram of the simulated wheat flour suggested that the starch and gluten structure were irregular and crystalline, and the glass transition temperature was higher than the whole wheat flour, indicating a higher degree of crystallinity and stability. 50 untrained panellists performed the sensory evaluation, and the panellists accepted the Indian flatbread prepared from the simulated wheat flour. The development of the simulated wheat flour formulation was successful, and it provides a promising solution for diabetic patients and people dealing with obesity.
... Foods that are low on the glycemic index (GI) scale tend to release glucose slowly and steadily, whereas foods high on the GI scale release glucose rapidly. Interestingly, consumption of macaroni provided significantly greater GI (68 ± 8) contents than did consumption of spaghetti (45 ± 6) [50]. Since macaroni has a moderate GI value, it has been suggested for and offered to patients with diabetes or obesity [51]. ...
Full-text available
Macaroni is a commercially available Italian food product that is popular among consumers around the world. The supplementation of green tea extract (GTE) and turmeric curcumin extract (TCE) in macaroni may serve as promising and beneficial bioactive ingredients. We aimed to produce functional macaroni, assess the degree of consumer satisfaction and study the antidiabetic activity in diabetic rats. In this study, macaroni was fortified with GTE, TCE and a mixture of GTE and TCE ratio of 1:1, w/w (GTE/TCE)., The resulting products were then analyzed in terms of their chemical compositions, while the degree of consumer satisfaction was monitored and the hypoglycemic and hypolipidemic effects in streptozotocin (STZ)-rats were investigated. GTE/TCE-M exhibited the strongest antioxidant activity (p < 0.05), while phenolics were most abundant in GTE-M. The overall preference for GTE-M, TCE-M and GTE/TCE-M were within ranges of 4.7–5.1, 5.9–6.7 and 6.2–8.2, respectively, in the nine-point hedonic scale. Consumption of these three preparations of macaroni (30 and 300 mg/kg each) neither decreased nor exacerbated increasing blood glucose levels in diabetic rats, while GTE-M (30 mg/kg) tended to lower increased serum triglyceride and cholesterol levels. In conclusion, GTE/TCE-M containing high amounts of bioactive EGCG and curcumin exerted the strongest degree of antioxidant activity and received the highest level of acceptance. Importantly, consumption of GTE-M tentatively ameliorated serum lipid abnormalities in diabetic STZ-induced rats by inhibiting lipase digestion and lipid absorption. Herein, we are proposing that GTE-fortified macaroni is a functional food that can mitigate certain metabolic syndromes.
... Comparing the resistant starch generated when cooking rice noodles with different techniques (boiled, steamed, microwaved, sautéed, and fried), it was observed that microwave cooking resulted in the highest content of resistant starch (0.99 g/100 g), a substantially higher content compared to the rest of the cooking methods analyzed: sautéed noodles (0.59 g/100 g), steamed noodles (0.44 g/100 g), and boiled noodles (0.43 g/100 g). Therefore, microwave cooking resulted in a higher content of resistant starch and, therefore, its consumption would cause a lower GR compared to the rest of the cooking techniques analyzed (27,62). However, the amount of resistant starch generated by cooking is less than when using post-cooking refrigeration methods (see section "Storage"). ...
Full-text available
Diet plays a critical role in the management of many chronic diseases. It is well known that individuals with type 2 diabetes (T2D) need to pay close attention to foods rich in carbohydrates to better manage their blood sugar. Usually, individuals are told to increase their dietary fiber intake which is associated with better glycemic control and limit their overall carbohydrate consumption. However, there are many other cooking strategies available to reduce the glycemic response to meals rich in carbohydrates and with a high glycemic index, such as adding fats, proteins, or vinegar, modifying the cooking or preparation processes, and even the selection and storage of foods consumed. The aim of the present narrative review is to summarize some of these existing strategies applied to the cooking process and their ability to modulate glycemic response to meals in individuals with T2D.
... La combinaison de niveaux d'humidité élevés et de températures élevées (par exemple pendant la cuisson ou le séchage au tambour) ou la haute pression et le cisaillement (comme dans la cuisson par extrusion) va entraîner un changement de structure de l'amidon : le SDS devient alors plus rapidement digestible [440], alors qu'un degré inférieur de gélatinisation ou le gonflement limité des granules d'amidon, qui est principalement déterminé par des niveaux d'humidité modérés, le temps de cuisson et la température, préserve la teneur en SDS comme pour le riz étuvé et certains type de biscuits et de pâtes [453,454]. Selon les procédés utilisés, le ratio SDS/RDS du produit final va être modifié et c'est finalement ce ratio qui va moduler la réponse glycémique. Pour illustrer l'impact de ces facteurs, Vinoy et al. [440] ont produit trois produits céréaliers transformés selon trois méthodes différentes : des biscuits (cuisson au four), du pain blanc (panification) et des céréales extrudées (cuisson au four-extrusion). ...
L’hyperglycémie chronique est impliquée dans le développement de complications associées au DT2 et la variabilité glycémique (VG) apparait comme une composante à part entière de l'homéostasie du glucose. Les mesures hygiéno-diététiques, en première ligne dans la prise en charge du DT2, passent entre autres par une modification de l’alimentation, dans laquelle les glucides occupent une place prépondérante. Au-delà de la quantité, la qualité des glucides a été mise en avant comme ayant un impact déterminant sur les excursions glycémiques. Notamment, la digestibilité des produits à base d’amidon pourrait alors avoir un impact sur le contrôle glycémique chez les patients atteints de DT2. Mais il y a aujourd’hui un réel besoin d’apporter une caractérisation des produits plus complète sur cet aspect et de mener des études de faisabilité et d’efficacité de tels régimes modulant la digestibilité de l’amidon. Mes travaux de thèse montrent qu’il est possible de concevoir un régime riche en amidon lentement digestible (SDS), grâce à des choix de produits amylacés disponibles dans le commerce, des conseils de cuisson et des recommandations adaptées. Pour la première fois, nous avons montré que le contrôle de la digestibilité de l'amidon de produits amylacés avec des instructions de cuisson appropriées dans une population atteinte de DT2 augmentait la consommation de contenu en SDS dans un contexte de vie réelle et que ce type de régime était bien accepté dans telle population. De plus, nous avons montré que l’augmentation du rapport SDS/glucides était associée à une amélioration du contrôle glycémique postprandial et qu’il existait une corrélation linéaire inverse entre les paramètres de VG et la teneur en SDS. La mise en œuvre d’un régime riche en amidon lentement digestible dans une population atteinte de DT2, a montré une différence significative sur le profil de variabilité glycémique, mais également sur les excursions glycémiques postprandiales, évalués par le CGMS, en comparaison avec un régime pauvre en amidon lentement digestible. Ce type de régime a également permis aux patients d’atteindre des cibles glycémiques postprandiales plus appropriées. Grâce à un travail de revue de la littérature, nous avons mis en évidence que la déviation standard (SD), le coefficient de variation (CV), l’amplitude moyenne des excursions glycémiques (MAGE) et la moyenne glycémique (MBG) étaient les paramètres de VG les plus étudiés en termes de relation avec les paramètres de diagnostic du DT2 et les complications liées au DT2 et qu’ils montraient des relations fortes, en particulier avec l’HbA1c. Dans les études interventionnelles, nous avons pu voir que la SD, le MAGE et le temps dans la cible (TIR) étaient les paramètres les plus utilisés comme critères d’évaluation, montrant des améliorations significatives suite aux interventions pharmacologiques ou nutritionnelles, souvent en lien avec des paramètres de contrôle glycémique comme l’HbA1c, la glycémie à jeun ou en postprandial. La VG apparaît donc comme une composante clé de la dysglycémie du DT2. Au-delà de son utilisation par le patient comme support du contrôle glycémique, le CGMS apparait comme un outil pertinent en recherche clinique pour évaluer l’efficacité des interventions même si à ce jour, il reste encore très peu utilisé pour les interventions nutritionnelles. Des études plus approfondies seront cependant nécessaires pour confirmer l'impact bénéfique de telles interventions alimentaires à long terme. Nous avons conçu une étude à plus grande échelle pour étudier l'impact à long terme d’un régime riche en SDS sur la variabilité et le contrôle glycémiques (CGMS) et les complications et comorbidités associées chez le patient atteint de DT2. La modulation de la digestibilité de l'amidon dans l'alimentation pourrait alors être utilisée comme un outil nutritionnel simple et approprié pour améliorer l'homéostasie glucidique au quotidien dans le DT2.
... Le Dockounou optimisé (IG=79) et le Dockounou traditionnel (IG=81) ont montré des valeurs IG presque similaires et l'analyse statistique n'a pas révélé de différences entre les valeurs d'IG. Les valeurs d'IG des aliments testés ont été identifiées comme étant des IG élevés[30],[31]. Ces gâteaux sont donc considérés comme des aliments hyperglycémiants. ...
Full-text available
Des données récentes recueillies à partir de l'optimisation de la fabrication du gâteau dockounou ont montré qu'un dockounou optimisé pourrait être un excellent moyen de valorisation de la banane plantain trop mûre. Mais, à ce jour, il existe peu de données scientifiques sur les propriétés glycémiques de cet aliment, qui est intégré dans les habitudes alimentaires urbaines. Nous avons donc déterminé l'index glycémique et la charge glycémique d’un dockounou traditionnel et le dockounou optimisé. En outre, nous avons réalisé leur profilage glucidique, dont la présence dans ces mets n’a été que peu étudiée. Dans l'ensemble, il n'y a pas eu de variations considérables dans leur composition nutritionnelle, à l'exception des cendres et des glucides. Les valeurs suivantes ont été obtenues pour les composantes glucidiques (g/100 g): amidon (32,4-33,7); saccharose (4,1- 8,8); glucose (20,1-20,7); fructose (21,1-22,6); fibres solubles (2,5-2,5) et fibres insolubles (6,9-6,9). L’index glycémique et la charge glycémique étaient respectivement de 79 et 20,4 pour le dockounou optimisé et respectivement de 81 et 21,3 pour le dockounou traditionnel. Ces résultats confirment bien que ces aliments sont une bonne source de glucides, notamment de la fraction glucose. Cependant, les niveaux élevés d'index glycémique et de charge glycémique impliquent une consommation avec modération.
... Two different types of pasta (spaghetti and penne), which represent the 2 best-selling types, were analyzed for potential differences due to shapes and sizes. This comparison demonstrated a lower glycemic response of the long pasta (spaghetti) compared with the short pasta (penne), as previously found (27,28). There were no clear differences in the amount of saliva, particle size distribution, or starch hydrolysis rates of spaghetti and penne boluses that could explain the difference seen in postprandial responses. ...
Full-text available
Background: Structure and protein-starch interactions in pasta products may be responsible for lower postprandial glycemic responses compared with other cereal foods. Objective: We tested the effect on postprandial glucose metabolism induced by two pasta products, couscous and bread, through their structural changes during mastication and simulated gastric digestion. Methods: Two randomized controlled trials (n = 30/trial) in healthy normal weight adults (23.9 and 23.0 kg/m2) evaluated postprandial glucose metabolism modulation to 50g of available carbohydrate portions of durum wheat semolina spaghetti, penne, couscous, and bread. A mastication trial involving 26 normal weight adults was conducted to investigate mastication processes and changes in particle size distribution and microstructure (light microscopy) of boluses after mastication and in vitro gastric digestion. Results: Both pasta products resulted in lower areas under the 2h-curve for blood glucose (-40% for spaghetti and -22% for penne vs couscous; -41% for spaghetti and -30% for penne vs bread), compared with the other grain products (P < 0.05). Pasta products required more chews (spaghetti: 34 ± 18; penne: 38 ± 20; bread: 27 ± 13; couscous: 24 ± 17) and longer oral processing (spaghetti: 21 ± 13 s; penne: 23 ± 14 s; bread: 18 ± 9 s; couscous: 14 ± 10 s) than bread or couscous (P < 0.01). Pastas contained more large particles (46-67% of total particle area) compared to bread (0-30%) and couscous (1%) after mastication and in vitro gastric digestion. After in vitro gastric digestion, pasta samples still contained large areas of non-hydrolyzed starch embedded within the protein network, protein in bread and couscous was almost entirely digested, and starch was hydrolyzed. Conclusions: Preservation of the pasta structure during mastication and gastric digestion explains slower starch hydrolysis and, consequently, lower postprandial glycemia compared to bread or couscous prepared from the same durum wheat semolina flour in healthy adults. Postprandial in vivo trials were registered at as NCT03098017 & NCT03104686.Clinical Trial Registry: NCT03098017 & NCT03104686
Full-text available
Type II diabetic subjects were given 50 g protein, 50 g glucose, or 50 g glucose with 50 g protein as a single meal in random sequence. The plasma glucose and insulin response was determined over the subsequent 5 h. The plasma glucose area above the baseline following a glucose meal was reduced 34% when protein was given with the glucose. When protein was given alone, the glucose concentration remained stable for 2 h and then declined. The insulin area following glucose was only modestly greater than with a protein meal (97 +/- 35, 83 +/- 19 microU X h/ml, respectively). When glucose was given with protein, the mean insulin area was considerably greater than when glucose or protein was given alone (247 +/- 33 microU X h/ml). When various amounts of protein were given with 50 g glucose, the insulin area response was essentially first order. Subsequently, subjects were given 50 g glucose or 50 g glucose with 50 g protein as two meals 4 h apart in random sequence. The insulin areas were not significantly different for each meal but were higher when protein + glucose was given. After the second glucose meal the plasma glucose area was 33% less than after the first meal. Following the second glucose + protein meal the plasma glucose area was markedly reduced, being only 7% as large as after the first meal. These data indicate that protein given with glucose will increase insulin secretion and reduce the plasma glucose rise in at least some type II diabetic persons.
Ten normal subjects ingested test meals based on apples, each containing 60 g available carbohydrate. Fibre-free juice could be consumed eleven times faster than intact apples and four times faster than fibre-disrupted purée. Satiety was assessed numerically. With the rate of ingestion equalised, juice was significantly less satisfying than purée, and purée than apples. Plasma-glucose rose to similar levels after all three meals. However, there was a striking rebound fall after juice, and to a lesser extent after purée, which was not seen after apples. Serum-insulin rose to higher levels after juice and purée than after apples. The removal of fibre from food, and also its physical disruption, can result in faster and easier ingestion, decreased satiety, and disturbed glucose homoeostasis which is probably due to inappropriate insulin release. These effects favour overnutrition and, if often repeated, might lead to diabetes mellitus.
A collaborative study was conducted to determine the total dietary fiber (TDF) content of food and food products, using a combination of enzymatic and gravimetric procedures. The method was basically the same as published earlier (J. Assoc. Off. Anal. Chem. (1984) 67, 1044-1052), with changes in the concentration of alcohol and buffers, time of incubation, sample preparation, and some explanatory notes, all with the intent of decreasing the coefficient of variation (CV) of the method. Duplicate blind samples of soy isolate, white wheat flour, rye bread, potatoes, rice, wheat bran, oats, corn bran, and whole wheat flour were analyzed by 9 collaborators. TDF was calculated as the weight of the residue minus the weight of protein and ash. CV values of the data from all laboratories for 7 of the samples ranged from 1.56 to 9.80%. The rice and soy isolate samples had CV values of 53.71% and 66.25%, respectively; however, each sample contained only about 1% TDF. The enzymatic-gravimetric method for determining TDF has been adopted official first action.
There has been much interest in the use of the glycemic index (GI). A recent study reporting plasma glucose responses to mixed meals containing fat and protein concluded that the results were totally disparate from what would have been expected from published GI values of the foods fed. However, this conclusion was based upon an inappropriate assessment of the data using absolute rather than incremental blood glucose response areas. The present report demonstrates how data may be analyzed to make use of the GI values of individual foods to predict the GI of mixed meals (r = 0.987; p less than 0.02). It is concluded that the GI concept applies well to mixed meals containing fat and protein.
Unexpected plasma glucose responses to different mixed meals fed to normal and diabetic volunteers have recently been reported. We have therefore examined in normal volunteers the effect of mixing carbohydrate foods of different glycemic indices (GIs) without the addition of fat and protein. The observed GI of the mixed meal was within 2% of the expected value. In studies in the literature where fat and protein were added to mixed meals, the observed blood glucose responses also related significantly to the meal GIs calculated from the individual foods. Addition of fat and protein in the quantities used did not obscure this relationship. Studies to determine sources of error in comparing glycemic responses showed that type II diabetic patients displayed the least within-individual variation, and type I diabetic patients the most. Expression of results as the GI rather than as absolute glycemic response areas reduced by 50% the between-subject variation. The mean GI values of rice tested in type I and type II patients were similar (82 +/- 22 compared with 74 +/- 19) and the reproducibility 22 mo later in the same group of subjects was excellent (81 +/- 15 compared with 83 +/- 15). However, the lack of precise GI values for all foods fed in the test meals indicates a need for GI values to be derived for a wider range of individual foodstuffs. The GI approach to classifying foods according to physiologic effect may play a useful role in planning meals and diets in which specific blood glucose profiles are required.
The blood glucose response to feeding 50-g carbohydrate portions of white and wholemeal bread and white spaghetti was studied in a group of nine diabetic subjects. Blood glucose rises after white and wholemeal bread were identical, but the response after spaghetti was markedly reduced. These results emphasize that food form rather than fiber may be important in determining the glycemic response and that pasta may be a useful source of carbohydrate in the diabetic diet.
To examine whether the form of dietary carbohydrate influences glucose and insulin responses, we studied the glucose and insulin responses to five meals--each containing a different form of carbohydrate but all with nearly identical amounts of total carbohydrate, protein, and fat--in 10 healthy subjects, 12 patients with Type I diabetes, and 10 patients with Type II diabetes. The test carbohydrates were glucose, fructose, sucrose, potato starch, and wheat starch. In all three groups, the meal containing sucrose as the test carbohydrate did not produce significantly greater peak increments in the plasma concentration of glucose or greater increments in the area under the plasma glucose-response curves than did meals containing potato, wheat, or glucose as test carbohydrates. Urinary excretion of glucose in patients with diabetes was not significantly greater after the sucrose meal. The meal containing fructose as the test carbohydrate produced the smallest increments in plasma glucose levels, but the differences were not always statistically significant. In healthy subjects and patients with Type II diabetes, peak serum concentrations of insulin were not significantly different in response to the five test carbohydrates. Our data do not support the view that dietary sucrose, when consumed as part of a meal, aggravates postprandial hyperglycemia.
The effect on plasma glucose concentration of four different, approximately isocaloric breakfasts designed using the American Diabetes Association food exchange lists was studied in eight type II diabetic patients. The meals were estimated to contain similar amounts of carbohydrate, protein, and fat and were given in random order. The plasma glucose responses to the different meals were similar except for one meal. This meal resulted in a greater glucose increase but the latter could be explained by the substitution of banana for orange juice in the meal. Banana contains starch as well as fructose and glucose, whereas orange juice contains glucose, fructose, and sucrose. In regard to the postmeal glucose response, these data indicate that the ADA food exchange list is useful in meal planning, at least for breakfast.
A significant relationship was found between the rate of release of the sugars; glucose, maltose, and maltotriose from amylitic digestion of 10 foods tested in vitro (expressed as the digestibility index) and the blood glucose response to 50-g carbohydrate portions of the same foods eaten by diabetics (expressed as the glycemic index), (r = 0.815, n = 10, p greater than 0.01). The glycemic index related to both the palatability of the foods (r = 0.731, p less than 0.05) and their frequency of use (r = 0.698, p less than 0.05). However, in this group of motivated diabetics food use was not related directly to palatability, but rather to health belief (r = 0.689, p less than 0.05). The results suggest that carbohydrate foods of potential use to the diabetic may be identified by their in vitro digestion characteristics but to a large extent their acceptance will depend on health belief and possibly ease of preparation.
Recently diabetic patients have been encouraged to increase their carbohydrate intake, but exact details of which foods to use are lacking. To determine whether sufficiently large differences existed to justify more specific dietary advice, we compared the glycaemic responses to 50 g carbohydrate portions of different foods, taken as breakfast test meals by groups of five to seven diabetic patients. Two- to threefold differences were seen amongst the 15 foods tested. The glycaemic responses for spaghetti, 'All-bran', rice and beans were significantly below those for bread, while 'Cornflakes' were above. Factors predicted to influence this were without effect, including: substituting wholemeal for white bread, increasing substantially the simple sugars (using 'All-bran' or bananas instead of wholemeal bread) and doubling meal protein by adding cottage cheese to bread. Paired comparisons of the glycaemic response to the five legumes with those of the seven other starchy foods (breads, spaghetti, rice, Cornflakes, oatmeal porridge and potatoes) showed that the mean peak rise in blood glucose concentration and mean area under the glucose curve after beans were 23 and 28% lower, respectively, than the mean for the other foods (p less than 0.001). Such results suggest a potentially valuable role for dried leguminous seeds in carbohydrate exchanges for individuals with impaired carbohydrate tolerance. These large differences in blood glucose response to different food cannot at present be predicted directly from tables of chemical composition. Nevertheless, physiological testing may both aid in understanding the factors responsible and help selection of the appropriate carbohydrate foods for the diabetic diet.