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Application of glycemic index to mixed meals


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Plasma glucose and insulin responses to six different meals were determined and compared with values predicted by published glycemic indices of the component foods. The test meals were of different ethnic origins: Indian (lentil curry with rice), Italian (spaghetti bolognaise), Chinese (stir-fried vegetables and chicken with rice), Greek (lentil stew), Western (sirloin chop and vegetables); and Lebanese (sandwich with unleavened bread and hummos). Eight healthy volunteers were given 50 g carbohydrate portions of the above meals after an overnight fast. The glycemic and insulin indices were highest for the Lebanese meal and lowest for the Greek with significant differences among the meals (ANOVA, p less than 0.05). The observed glycemic indices correlated well with the predicted glycemic indices (r = 0.88, p less than 0.01) and insulin responses parallelled the glycemic responses (r = 0.83, p less than 0.05). These results suggest that the glycemic index approach will be useful in planning diets for diabetic people.
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Am J C/in Mar l988;47:53-6. Printed in USA. © 1988 American Society for CliniCal Nutrition 53
Application of glycemic index to mixed meals1’2
Irene Chew, BSc; Janette C Brand, PhD; Anne W Thorburn, BSc; and AS Truswell, MD
ABSTRACI Plasma glucose and insulin responses to six different meals were determined
and compared with values predicted by published glycemic indices of the component foods.
The test meals were ofdifferent ethnic origins: Indian (lentil curry with rice), Italian (spaghetti
bolognaise), Chinese (stir-fried vegetables and chicken with rice), Greek (lentil stew), Western
(sirloin chop and vegetables); and Lebanese (sandwich with unleavened bread and hummos).
Eight healthy volunteers were given 50 g carbohydrate portions of the above meals after an
overnight fast. The glycemic and insulin indices were highest for the Lebanese meal and lowest
for the Greek with significant differences among the meals (ANOVA, p<0.05). The observed
glycemic indices correlated well with the predicted glycemic indices (r =0.88, p<0.01) and
insulin responses paralleled the glycemic responses (r =0.83, p<0.05). These results suggest
that the glycemic index approach will be useful in planning diets for diabetic people. Am
J Clin Nutr l988;47:53-6.
KEY WORDS Glycemic index, insulin responses
During the last few years it has been found that the
same weight of carbohydrate in different foods can pro-
duce widely different blood glucose reponses (glycemic
index). Tables are now available that compare the gly-
cemic index ofmany different foods, the implication being
that this approach will be helpful in planning meals for
individuals with diabetes (1). The nutrition committees
ofthe American and Canadian diabetes associations rec-
ommended that diets for diabetic people be planned with
the glycemic index of foods in mind (2, 3).
The question naturally arises whether glycemic index
can be used to predict the glycemic and insulin responses
to mixed meals. Coulston et al (4) reported that the gly-
cemic responses to mixed meals containing different types
of carbohydrate sources did not differ significantly and
concluded that the glycemic index approach would have
little clinical utility. Wolever et al (5) refuted these con-
clusions by demonstrating that the observed glycemic re-
sponses could be predicted by the glycemic indices of the
component foods.
The objective of this study is to determine the plasma
glucose and insulin responses of normal individuals to
six different mixed meals and to compare the results with
the values predicted by published glycemic indices of the
individual foods. The sources ofcarbohydrate were chosen
to represent the starchy staples of various ethnic groups,
ranging from high glycemic index (potato) in the Western
meal to low glycemic index (lentils) in the Greek meal.
The test meals were of six different ethnic origins: Indian
(lentil and cauliflower curry with rice), Italian (spaghetti bob-
gnaise), Greek (lentil stew with bread roll), Chinese (stir-fried
vegetables and chicken with rice), Western (sirloin chop with
vegetables and mashed potatoes), and Lebanese (sandwich of
unleavened bread, hummos, falafel, and tabouleh). Each meal
provided ‘-50 g available carbohydrate, 16 g fat, 16 g protein,
and 1700 Id (400 kcal) with 48% ofenergy as carbohydrate, 35%
as fat, and 17% as protein as determined by food composition
tables (6) (Table 1). A drink of water was given with the meals
to give a total meal weight of ‘-600 g.
Eight healthy volunteers (four women, four men) aged 21-
32 y with normal glucose tolerance participated in the study.
Their mean ± SEM body mass index was 22.3 ± 0.8 kg/rn2. The
subjects consumed each meal or 50 g glucose in randomized
order after an overnight fast on separate mornings 1 wk apart.
The meals were given at --0900 h and were eaten over 20 mm
with zero time as the time eating commenced. Fingerprick blood
samples were taken from warmed hands using an Autolet device
(EMP Surgical Pty Ltd. Collingwood, Victoria, Australia) at 0,
20, 30, 60, 90, 120, 1 50, and 1 80 mm. A 500-800 #L aliquot
ofcapillary blood was collected and mixed with 0.4 mg EDTA.
1From the Human Nutrition Unit, Department ofBiochemistry and
School of Public Health and Tropical Medicine, University of Sydney,
Sydney, Australia.
2Address reprint requests to Dr J Brand, Human Nutrition Unit,
008, University of Sydney, NSW 2006, Australia.
Received August 27, 1986.
Accepted for publication February 3, 1987.
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The nutritional composition of the meals
Chinese Indian Greek Italian Western Lebanese
Meal weight (g) 245 360 489 360 361 166
Energy(kJ) 1709 1685 1643 1680 1716 1717
(kcal) 407 401 391 400 409 409
Protein(g) 17 17 17 17 17 14
Fat(g) 16 16 15 17 15 16
Available carbohydrate (g) 515 1 50 48 53 50
Principal carbohydrate (g)
Rice 44 36
Lentils 13 24
Bread 16
Spaghetti 39
Potato 45
Lebanese bread 40
Chick peas 4
Plasma was removed after centrifugation (12 500 X g, I mm)
and stored at -80 #{176}C.Glucose was assayed by the glucose hexo-
kinase assay using the Glucose Rapid Centrifichem System
(Roche Diagnostica, Basle, Switzerland) and insulin by radio-
immunoassay (BioRIA, Montreal, Canada).
The glycemic index and insulin index of each meal in each
subject was determined using the following equations:
incremental area under 2 h
. . plasma glucose curve for meal x 100
Glvcemic index =. (1)
incremental area under 2 h
plasma glucose curve for 50 g glucose
incremental area under 2 h
.plasma insulin curve for meal X 100
Insulin index =incremental area under 2 h
plasma insulin curve for 50 g glucose
Results have been expressed as the mean ± SEM for the eight
subjects. Two-way analysis of variance (ANOVA) was used to
determine whether significant differences existed between the
glycemic and insulin indices ofthe meals. The observed glycemic
mndices were compared with those predicted by the published
glycemic indices of the component foods (1) and with the gly-
cemic index of white bread used for Lebanese bread. The pre-
dicted values were calculated by summing the percent carbo-
hydrate contributions ofthe component carbohydrate-containing
foods (Table 2) multiplied by the published glycemic index value
for the single foods (1). Linear regression was used to measure
correlation between variables.
The study was approved by the Medical Ethical Review Com-
mittee of the University of Sydney.
The mean glycemic and insulin response curves for the
(2) meals are shown in Figures 1and 2. The calculated gly-
cemic indices ranged from 40 ± 5 for the Greek meal to
86 ± 12 for the Lebanese meal (Table 2) with significant
differences among the meals (p <0.05). Insulin indices
ranged from 1 17 ± 33 for the Italian meal to 243 ± 74
for the Lebanese meal (Table 2)with significant differences
among the meals (p <0.05). There was a positive cor-
relation between the glycemic index and insulin index of
Predicted and observed glycemic and insulin indices ofthe meals and contribution ofthe carbohydrate-riCh foods to total carbohydrate content
Meal Contribution to
total carbohydrates Predicted
glycemic index Observed
glycemic index Observed
insulin index
%mean ±SEM mean ±SEM
Greek Lentils 48
Bread 32 38 40± 5 143±36
Italian Spaghetti 81 40 52± 9 117±33
Indian Lentils 26
Rice 71
60 60±10 188±63
Chinese Rice 86 65 73±17 188±58
Western Potato 85 69 66±12 226±78
Lebanese Lebanese bread 80
Chick peas 869 86 ±12 243 ± 74
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J- Western
- Lebanese
.- Italian
f___ -
02030 60
Time (mm) 90 120
FIG 1. Mean glycemiC responses to each ofthe meals.
...- Western
.s 20
the meals (r =0.83, p <0.05) and between the observed
glycemic index and the predicted glycemic index (r =0.88,
p<0.01, Table 2).
This study shows that there are significant differences
in the glycemic and insulin responses of healthy individ-
uals to different mixed meals. Moreover, the glycemic
indices of the mixed meals could be predicted from the
glycemic indices of the component carbohydrate foods.
Rank orders coincided for measured and predicted values
almost completely (Table 2). These results are contrary
to the report by Coulston et al (4) in which there were no
significant differences in the glycemic and insulin re-
sponses to three of the four mixed meals. There are im-
portant differences, however, between the two studies.
Coulston et al used individuals with noninsulin-dependent
diabetes and gave standardized meals containing a much
smaller amount ofthe varying test carbohydrate (30 g vs
39-49 g). The contribution of the test carbohydrate to
the total energy of the meal was therefore smaller (30%
vs 39-46%) and conversely the fat contribution was higher
(40% vs 35%). Because there are diurnal variations in oral
020 30 60 90
Time (mm)
FIG 2. Mean insulin responses to each of the meals.
glucose tolerance (7), it is also relevant that Coulston’s
meals were fed at 1200 h compared with 0900 h in this
study. Furthermore, unlike in Coulston’s study we did
not standardize the protein and fat sources. These factors
together may account for the difference in outcome be-
tween the two studies. Our study is in agreement with
other recent studies (5, 8) that have also shown that the
glycemic index approach is successful in predicting the
glycemic responses of healthy individuals as well as dia-
betic subjects to mixed meals.
Plasma insulin responses are also important in the study
ofphysiological responses to foods. Some meals have been
shown to give divergent plasma insulin responses in the
face ofsimilar plasma glucose responses (9). In our study,
the insulin indices correlated well with the glycemic in-
dices although they did not fall into the same rank order.
All the meals gave higher plasma insulin responses than
did oral glucose, which suggests that factors such as pro-
tein, fat, and other components of a mixed meal act as
insulin secretagogues.
Increasing evidence suggests that diets high in carbo-
hydrate and fiber and low in fat are beneficial in improving
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carbohydrate metabolism in individuals with diabetes
(10). The findings of this study along with other recent
reports suggest that choice of carbohydrate-rich food is
important. The reduction of postprandial hyperglycemia
and hyperinsulinemia is an important treatment goal and
dietary management should be based on the sound
knowledge of plasma glucose and insulin responses to
mixed meals. The incorporation of low-glycemic index
carbohydrate foods such as legumes into the diet has
been shown to reduce both the postprandial and 24 h
glucose profile in individuals with diabetes (1 1). Unfor-
tunately legumes are not a major component of Western
diets and the required change in diet pattern may be dii’-
ficult to achieve. Palatability may be increased by using
a combination of low- and high-glycemic index foods as
was used in this study: the Greek meal (lentil soup) in-
eluded a bread roll and the Indian lentil curry included
rice. Although these results need to be confirmed in in-
dividuals with diabetes and with many more mixed meals,
they suggest that the glycemic index approach will be use-
ful in dietary planning. Other aspects such as fat content
need to be considered (12) and glycemic responses to
meals may also vary with the method of cooking and
processing (13).
Our results do not, of course, imply that all Greek and
Italian meals will give lower glycemic indices than Leba-
nese and Western meals. Nonetheless it is interesting to
speculate that the increasing prevalence of diabetes in
many countries may be partly related to fast-release nature
of the staple carbohydrate foods. We have suggested that
the slow-release nature of the traditional foods of Asia,
India, Oceania, and Southern Europe may be protective
against diabetes (14), particularly in those populations
which show a genetic predisposition to hyperinsulinemia.
Long-term studies oflow- and high-glycemic index diets
on carbohydrate metabolism in normal and diabetic in-
dividuals are therefore needed. B
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foods: a physiological basis for carbohydrate exchange. Am J Gin
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2. American Diabetes Association. Policy statement glycemiC effects
ofeffects ofcarbohydrates. Diabetes Care l984;7:607-8.
3. Special Report Committee, Canadian Diabetes Association. Guide-
lines for the nutritional management of diabetes mellitus. J Can
Diet Assoc l981;42:1 10-8.
4. Coulston AM, Hollenbeck CB, Liu GC, et al. Effects of source of
dietary carbohydrate on plasma glucose, insulin and gastric inhibitory
polypeptide responses to test meals in subjects with noninsulin-
dependent diabetes mellitus. Am J Gin Nutr 1984;40:965-70.
5. Wolever TMS, Nuttall FQ, Lee R, et al. Prediction of the relative
blood glucose response ofmixed meals usingthe white bread glycemic
index. Diabetes Care l985;8:4l8-28.
6. Paul AA, Southgate DAT. MeCance and Widdowson’s the com-
position offoods. London: Her Majesty’s Stationery Office, 1979.
7. Jarrett RJ, Baker IA, Keen H, Oakley NW. Diurnal variation in
oral glucose tolerance: blood sugarandplasma insulinlevels morning,
afternoon and evening. Br Med J 1972;1:l99-201.
8. Collier GR, Wolever TMS, Wong GS, Josse RG. Prediction of gly-
cemic response to mixed meals in noninsulin-dependent diabetic
subjects. Am J Clin Nutr l986;44:349-52.
9. Collier G, McLean A, O’Dea K. Effect ofco-ingestion of fat on the
metabolic responses to slowly and rapidly absorbed carbohydrates.
Diabetologia 1984;26:50-4.
10. Mann JI. Lines to legumes: changing concepts ofdiabetic diets. Di-
abetic Med 1984;l:19l-9.
11. Simpson HCR, Simpson RW, Lousley 5, et al. A high carbohydrate
(leguminous fibre)diet improvesall aspectsofdiabeticcontroL Lancet
12. Thorburn AW, Brand JC, Truswell AS. The glycemic index of foods.
Med J Aust l986;l44:580-2.
I 3. Brand JC, Nicholson PL, Thorburn AW, Truswell AS. Food pro-
cessing and the glycemic index. Am J Gin Nutr l985;42:l 192-6.
14. Thorburn AW, Brand JC, Truswell AS. Slowly digested and absorbed
carbohydrate in traditional bushfoods: a protective factor against
diabetes. Am J Gin Nutr l987;45:98-l06.
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... Both high (>100 g) and low (<100 g) lentil serving sizes had a favorable postprandial blood glucose and insulin lowering effect, making it difficult to identify the optimal lentils' serving size for beneficial dose response effects [34]. Results of the current investigation are consistent with another study that reported the lowest GI and glycemic responses after consumption of lentil soup with bread roll compared to five other starchy mixed meals [35]. Another study found a 71% reduction in glycemic responses after lentils with butter and tomatoes consumption compared to two wholemeal bread meals [36], similar to the 73% reduction observed in the current investigation. ...
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Low glycemic index (GI) diets have been associated with decreased chronic disease risk. In a randomized, cross-over study we investigated the GI and glycemic response to three traditional Greek mixed meals: Lentils, Trahana, and Halva. Twelve healthy, fasting individuals received isoglucidic test meals (25 g available carbohydrate) and 25 g glucose reference, in random order. GI was calculated and capillary blood glucose (BG) samples were collected at 0–120 min after meal consumption. Subjective appetite ratings were assessed. All three tested meals provided low GI values. Lentils GI was 27 ± 5, Trahana GI was 42 ± 6, and Halva GI was 52 ± 7 on glucose scale. Peak BG values were lowest for Lentils, followed by Trahana and then by Halva (p for all <0.05). Compared to the reference food, BG concentrations were significantly lower for all meals at all time-points (p for all <0.05). Lentils provided lower glucose concentrations at 30 and 45 min compared to Trahana (p for all <0.05) and at 30, 45, and 60 min compared to Halva (p for all <0.05). BG concentrations did not differ between Trahana and Halva at all time points. No differences were observed for fasting BG, time to peak rise for BG, and subjective appetite ratings. In conclusion, all three mixed meals attenuated postprandial glycemic response in comparison to glucose, which may offer advantages to glycemic control.
... Other concerns related to dietary GI/GL estimation are whether foods consumed together may have an impact on each other to alter the GI/GL of the whole meal [16,68,69]. While some authors suggest that the GI of a meal can be calculated by adding the carbohydrate contributions of each constituent food multiplied by its published GI [13,70], another school of thought argues that a food is more than just the sum of its nutrients due to several chemical and physical interactions that may occur. Combining macronutrients was found to influence GI, the latter being positively associated with carbohydrate content and negatively associated with protein and fat content, which can significantly reduce the glycemic response [71]. ...
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High dietary glycemic index (GI) and glycemic load (GL) were suggested to increase the risk of metabolic syndrome (MetS). This study aims to estimate dietary GI and GL in a sample of healthy Lebanese adults and examine their association with MetS and its individual abnormalities. The study uses data from a community-based survey of 501 Lebanese urban adults. Dietary intake was assessed using a food frequency questionnaire. Biochemical, anthropometric, and blood pressure measurements were obtained. Subjects with previous diagnosis of chronic disease, metabolic abnormalities, or with incomplete data or implausible energy intakes were excluded, yielding a sample of 283. Participants were grouped into quartiles of GI and GL. Multivariate logistic regression analyses were performed. Average dietary GI and GL were estimated at 59.9 ± 8 and 209.7 ± 100.3. Participants belonging to the highest GI quartile were at increased risk of having MetS (odds ratio (OR) = 2.251, 95% CI:1.120–4.525) but this association lost significance with further adjustments. Those belonging to the second quartile of GI had significantly lower odds of having hyperglycemia (OR: 0.380, 95% CI:0.174–0.833). No associations were detected between GL and MetS. The study contributes to the body of evidence discussing the relationship between GI, GL, and MetS, in a nutrition transition context.
... Meal patterns may affect the absolute glycemic response but do not affect the relative differences between foods (3)(4)(5). Metabolic studies using standardized methods indicated that the correlation between the glycemic index of mixed meals and the average glycemic indexes of individual component foods ranges from 0.84 to 0.99 (5)(6)(7). Even though the total quantity of the glycemic and insulinemic effects of foods may not be fully captured by dietary glycemic load, these measurement errors were likely to have been modest and unrelated to CHD because diets were assessed before disease occurred. ...
... In practice, most meals contain much greater amounts of fat and protein than were fed here. Nonetheless, differences in GI have been found to predict the glycemic response to realistic mixed meals and daylong glycemia (29,30). In persons with diabetes, low-GI diets have been found to lower glycated hemoglobin, a measure of the average blood glucose concentration over the previous 2-3 mo (31). ...
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Background: Growth in normal and malignant tissues has been linked to hyperinsulinemia and insulin-like growth factors (IGFs). We hypothesized that IGF and IGF-binding protein (IGFBP) responses may be acutely affected by differences in the glycemic index (GI) of foods. Objective: We compared the postprandial responses of IGFs and IGFBP to 2 foods of similar macronutrient composition but with greatly different GIs—pearled barley (GI: 25) and instant mashed potato (GI: 85). Design: Ten young lean subjects consumed 50-g carbohydrate portions of the 2 foods or water (extended fast) in random order after an overnight fast. Capillary blood was collected at regular intervals over 4 h for measurement of blood glucose, insulin, and components of the IGF system. Results: Serum IGFBP-1 declined markedly after both meals, but the mean (±SEM) change at 4 h was significantly (P < 0.01) more prolonged after the low-GI meal (−55 ± 20 ng/mL) than after the high-GI meal (−13 ± 15 ng/mL). Conversely, the change in serum IGFBP-3 concentration at 4 h was significantly (P < 0.05) higher after the low-GI meal (251 ± 102 ng/mL) than after the high-GI meal (−110 ± 96 ng/mL); the same pattern was observed at 2 h. Changes in IGFBP-2, free IGF-1, and total IGF-1 responses were minimal and did not differ significantly from those during the 4-h fast. Conclusion: Acute changes in IGFBP-3 after low-GI and high-GI foods may provide a biologic mechanism linking cell multiplication with greater consumption of high-GI carbohydrates.
... The prediction of glycemic response to meals containing fat and protein also can successfully be predicted by GI 21 . This demonstrates that GI can be applicable into mixed meals 22 . Having said that , integrating information about the GI of foods into Malaysian diet need more data. ...
Conference Paper
Acne vulgaris is one of the most common skin diseases seen by physicians. It has been postulated that the high glycemic index (GI) foods play an important role in the pathogenesis as well as recovery of acne vulgaris. Furthermore, the multicultural diversity gives rise to a variety of food preferences amongst Malaysians. Thus, this study was aimed to retrospectively identify the clinical impacts of low glycemic index diet in treatment of severe acne vulgaris among Malaysian youths residing in Malacca. The cases were retrieved from medical reports in SkiMed Clinic located in Malacca. A total of 20 patient records were reviewed based on the inclusion and exclusion criteria. Out of the 20 patients, 10 received dietary counseling for low GI foods (study group) and the other 10 had no dietary counseling (control group). Data analysed included demographic factors, anthropometric measurements, HbA1c, fasting sugar and global acne assessment score (GAAS) which were collected on the first and 4th visit at week 12 of the follow up. Patients’ age group were 20-25 years old, predominantly Chinese (19 individuals) with mean BMI of 23.62 to 24.21. Majority of patients have Fitzpatrick skin type III (16 individuals) followed by type IV (4 individuals). In the first visit the GAAS was 43.4 and 41.4 whilst in the 4th visit it was 15.8 and 28.4 in the study and control groups respectively. The mean serum HbA1c level was 5.1 in both groups in the first visit; however, in the 4th visit it dropped to 4.9 in the study group and increased to 5.4 in control group. There was a drop of mean fasting sugar from 5.3mg/dL on the first visit to 4.96mg/dL at the 4th visit in the study group. In conclusion, adopting a low glycemic index diet in these patients revealed a greater improvement in the recovery of severe acne vulgaris than the regular Malaysian diet.
... This process moves the sugar from the bloodstream so it can be used as energy, thereby increasing subsequent overall hunger and decreasing satiety levels (Ludwig, 2002;Page et al., 2011;Pittas et al., 2005). Indeed, high-glycemic foods, such as sugar-laden beverages, are absorbed rapidly in the gastrointestinal tract, leading to a sharp increase in glucose (Benelam, 2009;Chew, Brand, Thorburn, & Truswell, 1988;Granfeldt, Bj€ orck, & Hagander, 1991). Several studies have shown that foods with a high (vs. ...
In this research, we examine the interplay between physiological and psychological factors that determine whether the sugar level of a preload increases or decreases consumption on a subsequent snack-eating task. In study 1, participants who drank a high-sugar protein shake (which they believed to be healthy) consumed more subsequent snacks than participants who drank a low-sugar protein shake. Study 2 replicated these findings, but only when the shake was labeled as "healthy." When the shake was labeled as "indulgent," the effect was mitigated.
In this article, the authors develop and analyze a linear programming model to obtain an ideal diet for individuals with diabetes by setting the glycemic load as the objective function. Additionally, a standardized system is used in order to facilitate the substitutability of foods present in a diet since those are classified according to their macronutrient content (proteins, lipids, and carbohydrates) and these values are, on average, very similar. Finally, the diet glycemic index is calculated with the model's outcome to corroborate that it is indeed a diet with low glycemic index and that, at the same time, it complies with the nutrient restrictions, which proves that the model can be a useful tool both to generate low glycemic index diets and to restrict certain nutrients from the diet.
Background: Obesity and metabolic abnormalities are important risk factors for knee osteoarthritis (KOA). Recent epidemiologic studies have found that a high glycemic index (GI) and glycemic load (GL) diet are associated with a higher risk for metabolic complications and cardiovascular mortality. Objective: We aimed to examine the association between dietary GI, dietary GL, and KOA among Korean adults. Design: This was a cross-sectional study that analyzed data obtained from the Korean National Health and Nutrition Examination Survey 2010-2012. Participants/setting: A total of 9,203 participants (5,275 women) aged ≥50 years were included. Main outcome measures: KOA was defined as the presence of radiographic features of Kellgren-Lawrence grade ≥2. Chronic knee pain was defined as the presence of knee pain for more than 30 days during the past 3 months. Dietary information was collected using a single 24-hour recall method. Statistical analyses performed: The association between the quintiles of dietary GI and dietary GL and knee conditions was analyzed using a multinomial logistic regression analysis adjusting for age, physical activity, obesity, hypertension and diabetes, serum low-density lipoprotein, and total energy intake. Results: Among the women, the association between dietary GI and symptomatic KOA was: quintile 1: 1.00 (reference); quintile 2: 1.29 (95% CI 0.87 to 1.92); quintile 3: 1.59 (95% CI 1.11 to 2.28); quintile 4: 1.74 (95% CI 1.21 to 2.51); and quintile 5: 1.77 (95% CI 1.20 to 2.60) (P=0.001). Chronic knee pain without KOA was associated with dietary GI; however, this association was not linear across quintiles. There was no significant association between dietary GI and asymptomatic KOA. Among the men, no significant association was found between dietary GI and any knee conditions. There was no significant association between dietary GL and KOA in both men and women. Conclusions: There was a significant positive association between dietary GI and symptomatic KOA in women.
Diet therapy, the cornerstone of gestational diabetes mellitus (GDM) management aims to promote adequate weight gain and ensure glycaemic control in the pregnant mother. Achievement of these maternal goals improves pregnancy and neonatal outcomes. The advocacy of low-GI foods is based on slower rate of carbohydrate absorption of these foods, which subsequently lowers postprandial glycaemic and insulinaemic responses. Glycaemic load (GL), a concept that merges carbohydrate quality and quantity of foods, accurately predicts postprandial glycaemia for single foods or mixed meals. Low-GI/GL diets have shown to improve management of body weight, glycaemia and cardiovascular risks, especially in hyperinsulinaemic and insulin-resistant populations. A 15% reduction in dietary GI bestows clinically significant health benefits, and this magnitude of GI reduction is made possible by substituting usual high-GI staples with lower GI alternatives, while maintaining their prescribed serving size. In this review, we assess the evidence for the treatment of GDM, a condition closely associated with hyperinsulinaemia and insulin resistance, with low-GI/GL diets.
Full-text available
Aims The traditional Italian dish pasta is major food source of starch with low glycemic index (GI), and also an important low-GI component of the Mediterranean diet. This systematic review aimed at assessing comprehensively and in-depth the potential benefit of pasta on cardio-metabolic disease risk factors. Data Synthesis Following a standard protocol, we conducted a systematic literature search of PubMed, CINAHL, and Cochrane Central Register of Controlled Trials, for prospective cohort studies and randomized controlled dietary intervention trials that examined pasta, and pasta-related fiber and grain intake in relation to cardio-metabolic risk factors of interest. Studies evaluating postprandial glucose response to pasta compared to bread or potato were quantitatively summarized using meta-analysis of standardized mean difference. Evidence from studies with pasta as part of low-GI dietary intervention and studies investigating different types of pasta were qualitatively summarized. Conclusions Pasta meals have significant lower postprandial glucose response compared to bread or potato meals, but evidence was lacking in terms of how the intake of pasta can influence cardio-metabolic disease risk. More long-term randomized controlled trials are needed where investigators directly contrast the cardio-metabolic effects of pasta and bread or potato. Long-term prospective cohort studies with required data available should also be analyzed regarding the effect of pasta intake on disease endpoints.
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The purpose of the study was to compare the in vitro starch digestibility and postprandial blood glucose response of conventionally-cooked versus factory-processed foods. Carbohydrate portions of three unprocessed foods (boiled rice, sweet corn, and potato) and six processed foods (instant rice, Rice Bubbles, corn chips, Cornflakes, instant potato, and potato crisps) were incubated for 3 h with human saliva and porcine pancreatin. The proportion of starch digested was significantly higher (p less than 0.05) for the processed forms of rice, corn, and potato compared with the respective conventionally cooked foods. In six healthy volunteers who ingested 50 g carbohydrate portions of the above foods the processed foods produced a higher glycemic index (p less than 0.05) in all but one instance. The exception was potato crisps which gave a similar glycemic response to boiled potato.
The glycaemic index of foods describes the hyperglycaemic effect of isolated foods. It is measured by the ratio of the area under the glycaemic curve observed after ingestion of a 50 gram carbohydrate dose of the tested food to the area observed after ingestion of the same amount of a reference food (white bread). The glycaemic index classification provides an approach of the diabetic diet based on the glucose response to foods. Widely accepted when applied to isolated foods, its clinical utility during mixed meals remains discussed. The improvement of the average metabolic control which can be expected from its use is modest. However, its major interest could be observed during snacks and meals in order to control precisely post-prandial glucose variations, in association with blood glucose self-monitoring. As it allows high sweet taste-low-glycaemic index-carbohydrate foods to be consumed, it could significantly improve the quality of life of diabetic patients.
The determine the effect of different foods on the blood glucose, 62 commonly eaten foods and sugars were fed individually to groups of 5 to 10 healthy fasting volunteers. Blood glucose levels were measured over 2 h, and expressed as a percentage of the area under the glucose response curve when the same amount of carbohydrate was taken as glucose. The largest rises were seen with vegetables (70 +/- 5%), followed by breakfast cereals (65 +/- 5%), cereals and biscuits (60 +/- 3%), fruit (50 +/- 5%), dairy products (35 +/- 1%), and dried legumes (31 +/- 3%). A significant negative relationship was seen between fat (p less than 0.01) and protein (p less than 0.001) and postprandial glucose rise but not with fiber or sugar content.
Effects of source of dietary carbohydrate on plasma glucose, insulin and gastric inhibitory polypeptide responses to test meals in subjects with noninsulindependent diabetes mellitus
  • Cb
  • G C Liu
CB, Liu GC, et al. Effects of source of dietary carbohydrate on plasma glucose, insulin and gastric inhibitory polypeptide responses to test meals in subjects with noninsulindependent diabetes mellitus. Am J Gin Nutr 1984;40:965-70.