Dietary glycaemic index and glycaemic load in the European Prospective Investigation into Cancer and Nutrition

Abstract

Objectives: To describe dietary glycaemic index (GI) and glycaemic load (GL) values in the population participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study according to food groups, nutrients and lifestyle characteristics. Methods: Single 24-h dietary recalls (24-HDRs) from 33 566 subjects were used to calculate dietary GI and GL, and an ad hoc database was created as the main reference source. Mean GI and GL intakes were adjusted for age, total energy intake, height and weight, and were weighted by season and day of recall. Results: GI was the lowest in Spain and Germany, and highest in the Netherlands, United Kingdom and Denmark for both genders. In men, GL was the lowest in Spain and Germany and highest in Italy, whereas in women, it was the lowest in Spain and Greece and highest in the UK health-conscious cohort. Bread was the largest contributor to GL in all centres (15-45%), but it also showed the largest inter-individual variation. GL, but not GI, tended to be lower in the highest body mass index category in both genders. GI was positively correlated with starch and intakes of bread and potatoes, whereas it was correlated negatively with intakes of sugar, fruit and dairy products. GL was positively correlated with all carbohydrate components and intakes of cereals, whereas it was negatively correlated with fat and alcohol and with intakes of wine, with large variations across countries. Conclusions: GI means varied modestly across countries and genders, whereas GL means varied more, but it may possibly act as a surrogate of carbohydrate intake.

Mediterranean diet and glycaemic load in relation to incidence of type 2 diabetes: results from the Greek cohort of the population-based European Prospective Investigation into Cancer and Nutrition (EPIC)

“…Food items containing negligible or no carbohydrates (chiefly meat, fish, fats and eggs) were not assigned any values. Foods that most contributed to the GL estimate were bread (40% among men and 31% among women), fruits (12% and 14%), pasta/rice/ other grains (9% and 8%), sugar and confectionary (7% and 9%), sweet buns/cakes/pies/biscuits (5% and 9%) and vegetables (5% and 5%) [29]. The correlation coefficient between dietary GL and total carbohydrates was 0.91.…”

“…GI values of foods were assigned to items reported in the dietary questionnaires in a standardised manner as described in detail elsewhere [29]. In brief, GI values assigned to individual food items were obtained from the FosterPowell table [28], British values [30] and internet updates (www.glycemicindex.com), using glucose as the reference.…”

Fibre intake and evolution of BMI: from pre-pregnancy to postpartum

“…To our knowledge, no other study has indicated this association. It is important to underscore the effects of white bread on glycaemic changes, acting as a possible obesity cofactor (44) . Foods with a high glycaemic index, like white bread, appear to cause less satiety, more hunger after meals and increase daily energy intake, while foods with a high fibre content have been proposed to be more satiating, reduce hunger and decrease energy intake (45) .…”

Lipotropes from plant-based foods supplied by a standard French diet vs. food guide pyramid recommendations: Grain products are the best sources at lower cost

“…In the Expanded Discussion (see Supplementary Materials), we have tried to define such a threshold by comparing the LC to the Nutrient Density Score (NDS), and have estimated a threshold of 30% above which PBF LC might be relevant. Ideally, human studies would be needed to define such a threshold like those performed for the glycaemic index (GI) whereby foods are clustered within three categories: low GI-foods with a GI below 55, intermediate GI-foods with a GI between 55 and 70 and high-GI foods with a GI above 70 (van Bakel et al, 2009)). Such a classification has important nutritional implications in that it helps guide food choices, e.g.…”