To describe dietary carbohydrate intakes and their food sources among 27 centres in 10 countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study.
Between 1995 and 2000, 36 034 subjects, aged between 35–74 years, were administered a standardized, 24-h dietary recall using a computerized interview software programme (EPIC-SOFT). Intakes (g/day) of total carbohydrate, sugars, starch and fibre were estimated using the standardized EPIC Nutrient Database (ENDB). Mean intakes were adjusted for age, total energy intake, height and weight, and were weighted by season and day of recall.
Adjusted mean total carbohydrate intakes were highest in Italy and in the UK health-conscious cohort, and were lowest in Spain, Greece and France. Total fibre intakes were highest in the UK health-conscious cohort and lowest in Sweden and the UK general population. Bread contributed the highest proportion of carbohydrates (mainly starches) in every centre. Fruit consumption contributed a greater proportion of total carbohydrates (mainly sugars) among women than among men, and in southern centres compared with northern centres. Bread, fruits and vegetables represented the largest sources of fibre, but food sources varied considerably between centres. In stratified analyses, carbohydrate intakes tended to be higher among subjects who were physically active, never-smokers or non-drinkers of alcohol.
Dietary carbohydrate intakes and in particular their food sources varied considerably between these 10 European countries. Intakes also varied according to gender and lifestyle factors. These data will form the basis for future aetiological analyses of the role of dietary carbohydrates in influencing health and disease.
Although dietary carbohydrates are an important source of energy, contributing between 40–70% of energy intake in most countries worldwide, their role in promoting health and disease is still relatively poorly understood (Mann et al., 2007). Evidence to date suggests that the influence of carbohydrates on chronic disease may depend on the relative proportions and absolute amount of different types of carbohydrates (that is, sugars, starch and fibre), their rates of absorption and digestion and their biological effects (Smith, 1994; Holt et al., 1997; Augustin et al., 2002; Mann et al., 2007). In particular, diets high in fibre may protect against type II diabetes (Meyer et al., 2000; Hu et al., 2001), cardiovascular disease (Rimm et al., 1996), colorectal cancer (Key and Spencer, 2007) and obesity (van Dam and Seidell, 2007). In contrast, diets rich in sugars, rather than starch, have been shown to have adverse effects on health, including dental caries (Selwitz et al., 2007), raised levels of triglycerides and insulin (Daly et al., 1997; Daly, 2003), obesity (van Dam and Seidell, 2007) and possibly some cancers (Cust et al., 2007; Key and Spencer, 2007).
Comparisons of carbohydrate intakes between countries are complicated by the wide variations in food sources of carbohydrates, as well as difficulties in standardizing measurement of dietary components because of differences in country-specific food composition tables (Deharveng et al., 1999). As a result, previous European epidemiological research on dietary carbohydrates has focused predominantly on consumption of carbohydrate-containing foods (Wirfalt et al., 2002) rather than nutrients, or on country-specific rather than inter-country nutrient intakes.
The European Prospective Investigation into Cancer and Nutrition (EPIC) calibration substudy used a computer-assisted 24-h dietary recall (24-HDR) method (EPIC-SOFT) to collect standardized dietary measurements from 36 994 participants in 10 European countries (Slimani et al., 2002b). The recent development of the EPIC Nutrient Database (ENDB) (Slimani et al., 2007) has harmonized separate nutrient databases from 10 European countries, and allows a comparison of dietary carbohydrate intakes (including total sugars, starch and fibre) between countries and population subgroups, for example, according to body mass index (BMI) categories, after accounting for potential confounders (for example, total energy intake). The ENDB will assist in identifying important differences in carbohydrate intakes and profiles across Europe, and in clarifying associations and the underlying biological mechanisms between diet, cancer and other diseases.
In this descriptive paper, we examine (1) the distribution of intakes of different types of carbohydrates, including total carbohydrates, sugars, starch and fibre, among the 27 redefined EPIC centres and in different population subgroups; and (2) the relative contribution of various food groups to carbohydrate intake.
Materials and methods
The EPIC calibration study was nested within the EPIC prospective cohort, which was designed to investigate the associations between diet, lifestyle and cancer in 10 European countries: Denmark, France, Germany, Greece, Italy, Norway, Spain, Sweden, The Netherlands and the United Kingdom (Riboli et al., 2002; Slimani et al., 2002b). EPIC participants were mostly recruited from the general population residing within defined geographical areas, with some exceptions: women members of a health insurance scheme for school employees (France); women attending breast cancer screening (Utrecht, The Netherlands); mainly blood donors (centres in Italy and Spain); and a cohort consisting predominantly of vegetarians (‘health-conscious’ cohort) in Oxford, UK (Riboli et al., 2002). Of the 27 EPIC centres redefined for dietary analyses, 19 had both female and male participants, and 8 centres (in France, Norway, Utrecht and Naples) recruited only women. The calibration study was undertaken between 1995 and 2000 to improve the comparability of dietary data across centres, and to partially correct for dietary measurement error arising from centre-specific bias and random and systematic within-person errors (Willett, 1998; Ferrari et al., 2004). The EPIC calibration study sample consists of a random sample of 36 994 participants (approximately 8% of the entire EPIC cohort of 520 000 participants) stratified by centre, age and gender, who were administered a standardized, computer-assisted 24-HDR.
A total of 36 034 subjects (13 025 men, 23 009 women) with 24-HDR data were included in this analysis, after exclusion of 960 subjects aged below 35 or above 74 years, because of few participants enrolled in these age categories. Approval for the study was obtained from the ethical review boards of the International Agency for Research on Cancer (Lyon, France) and from all local recruiting institutes. All participants provided written informed consent.
Measurements of diet and other lifestyle factors
The 24-HDR was administered in a face-to-face interview, except in Norway, where it was obtained by telephone interview (Brustad et al., 2003). A highly standardized computerized interview programme (EPIC-SOFT) was developed specifically for the calibration study (Slimani et al., 1999, 2000). Previous publications outline in detail the rationale, methodology and population characteristics of the 24-HDR calibration study (Slimani et al., 2002b; Kaaks et al., 1994, 1995).
Intakes (g/day) of total carbohydrates, sugars, starch and dietary fibre (including resistant starch) were estimated from the 24-HDR using country-specific food composition tables and other calculation processes, which were standardized as far as possible across countries to allow comparable intake estimates and calibration at the nutrient level. The ENDB project outlines in detail the methods used to standardize the national nutrient databases across the 10 countries, including matching of EPIC foods to national databases, deriving nutrient values of unavailable foods and imputing missing values (Slimani et al., 2007). Certain ENDB carbohydrate components were missing in the original national data sets (for example, Sweden), or had definitions differing from the reference ENDB definitions (for example, Denmark), which were corrected in the final national data sets evaluated (Slimani et al., 2007). After evaluation and imputation of missing values, the overall completeness of all national data sets was above 98%.
The definitions of total carbohydrates, sugars, starch and dietary fibre, and the methods used to determine their values in each centre and standardize values across centres have also been described in detail (Slimani et al., 2007). For some centres, missing data, or differences in the definitions and analytical methods used in the local nutrient databases across the 10 countries, led to the use of different calculation methods for values of total carbohydrates and their components. Briefly, total carbohydrates, excluding dietary fibre, were defined as the sum of analysed carbohydrate components, but in Denmark and to a lesser extent in Greece, missing carbohydrate values were calculated for the ENDB using the ‘by difference’ method (Englyst et al., 2007). In Italy, UK, Greece and Spain, all carbohydrate components were converted to grams from monosaccharide equivalents. Total sugars consisted of mono- and disaccharides (saccharose, lactose and maltose), and excluded trisaccharides (except in Denmark) and higher oligosaccharides. They include ‘added sugar’ and ‘free sugars’. Total starch included dextrins and glycogen. For foods of animal origin, except molluscs, starch was assigned zero value when missing. Dietary fibre was defined by the AOAC (Association of Official Agricultural Chemists) gravimetric method for Total Dietary Fibre (DeVries and Rader, 2005), and includes soluble and insoluble forms (including lignin) of non-starch polysaccharides (NSP) and resistant starch. The AOAC method is the reference labelling method in Europe. For fruits and vegetables, but not for potatoes and other tubers, the AOAC and NSP (Englyst method) values were assumed to be comparable (the NSP method was mostly used in the UK and Greece). In the UK, Greece and Spain, fibre was assigned a zero value for foods of animal origin.
Data on other lifestyle factors, including education level, total physical activity and smoking history, considered in this analysis were collected at baseline through standardized questionnaires and clinical examinations, and have been described for the calibration sample elsewhere (Riboli et al., 2002; Haftenberger et al., 2002a, 2002b; Slimani et al., 2002b). Data on age, as well as on body weight and height, were self-reported by participants during the 24-HDR interview. The mean time interval between these baseline questionnaire measures and the 24-HDR interview varied by country, from 1 day to 3 years later (Slimani et al., 2002b).
Data are presented as mean intakes and standard errors (s.e.), stratified by study centre and gender, and ordered according to a geographical south–north gradient. Intakes of total carbohydrates, sugars, starch and fibre are presented according to carbohydrate-rich foods. The food classification used was adapted from the EPIC-SOFT food subgroups described in detail elsewhere (Slimani et al., 2000, 2002a; Wirfalt et al., 2002). Food groups that contributed large amounts of carbohydrates (for example, cereals and cereal products) were further split into subgroups, whereas food (sub) groups that contributed very few carbohydrates were not presented (for example, sauces and condiments, and meat and fish products).
‘Minimally adjusted’ mean intakes were adjusted for age, and weighted by season and day of the week of recall using generalized linear models to control for different distributions of 24-HDR interviews across seasons and days of the week. We examined the independent effect of adjustment of several potential confounders—including height, weight, total energy intake, BMI, smoking status, highest education level and physical activity—on mean carbohydrate intakes, centre rankings and on the R2 of the model as an estimation of the percentage variability of nutrient intakes that is explained by the potential confounder. In ‘fully adjusted’ models, we retained total energy intake because it had a strong influence on mean carbohydrate intakes, and we retained height and weight (except for stratified analyses according to BMI), as they were considered a priori to be potentially important predictors of carbohydrate intakes, in addition to weighting by season and day of the week. P-trends across the age categories were computed. We also performed stratified analyses of dietary carbohydrates according to BMI category (<25, 25–<30 and ⩾30 kg/m2), physical activity (inactive, moderately inactive, moderately active and active), alcohol intake (0, >0–10, >10–30 and >30 g/day), smoking status (never, former and current), education level (primary, secondary/technical college and university), season and day of the week. These stratification factors were selected a priori as carbohydrate intakes were thought to potentially differ in these subgroups. Presentation of carbohydrate intakes by diabetes status was not possible because of few (3.6%) participants with this self-reported condition. Analyses were performed using SAS (version 9.1, SAS Institute, Cary, NC).
Minimally adjusted mean intakes of total carbohydrates, sugars, starch and fibre
Centre-specific mean carbohydrate intakes, adjusted for age and weighted by season and day of the week of recall, and stratified by gender, centre and age, are presented in Table 1a. Among men, total carbohydrate intakes ranged from approximately 200 g/day in Greece to just over 300 g/day in Italy. Among women, the lowest consumption of total carbohydrates was in Greece (154 g/day), whereas the highest was in the UK health-conscious cohort (237 g/day). Mean intakes of sugars, starch and fibre are displayed in Tables 1b, 1c and 1d. Both men and women in Greece consumed relatively low amounts of total sugars and starch. The overall mean starch intakes were higher for men than for women (range 119–216 g/day for men and 82–132 g/day for women). High intakes of starch were found in Italy for men (192–216 g/day) and women (119–132 g/day), and also in Umeå among women (121 g/day). The lowest starch intakes among men were in Potsdam and Greece (119 g/day), and among women in Greece, Granada and Navarra (82–86 g/day). Total fibre intake among both men and women was markedly higher in the UK health-conscious group than in the UK general population and was the lowest in Sweden. Overall, there was relatively little variation in carbohydrate intake across age categories. The percentage of total dietary energy derived from total carbohydrates ranged from 35 to 50% in men and from 38 to 50% in women across EPIC centres (Supplementary information is available on the EPIC website (http://epic.iarc.fr)). The highest proportion of total energy from carbohydrates was in Italian centres (46–49%) and in the UK health-conscious population (50%). More details on main nutrient energy sources are provided in a separate paper (Ocké et al., 2009).
Influence of potential confounders
The fully adjusted mean daily intakes of total carbohydrates, sugars, starch and fibre, adjusted for age, energy, weight and height, and weighted by day of 24-HDR and season, are presented in Figures 1a and b, and are additionally stratified by age in the Appendix (Tables A1a, A1b, A1c and A1d). The highest mean intakes of total carbohydrates were in Italy for men and in the UK health-conscious group for women, and the lowest were in Spain and Greece for men and in non-Italian Mediterranean centres for women. Adjustment for total energy intake had the greatest influence on mean total carbohydrate intakes, with increases in Greece of 13.6% for men and 18.2% for women, and decreases of 11.9% among men in San Sebastian and 10.0% among women in Aarhus (data not shown). Adjustment for other factors, including height and weight, changed total carbohydrate intakes by less than 5%, although many of these factors showed statistically significant (P<0.05) associations with carbohydrate intake. To test any gender-specific effect on carbohydrate consumption, we tested the interaction between centre and gender after adjustment for other co-variables; the sex-centre variable was statistically significant (P<0.0001) but explained only about 1% of the total variability of carbohydrate intake. All results were similar for total sugars, total starch and fibre (data not shown).
Intakes of total carbohydrates, sugars, starch and fibre according to carbohydrate-rich food subgroups
The means and proportions of total carbohydrates are presented for each of the carbohydrate-rich food subgroups, stratified by centre and gender, in Table 2a (men) and Table 2b (women). Results for sugar, starch and fibre intakes are available on the EPIC website (http://epic.iarc.fr).
A large proportion of the total carbohydrates consumed was derived from cereals and cereal products, with the highest values in Italy and Greece (around 50–60% among men and 45–50% among women). Bread contributed the highest proportion of carbohydrates in every centre: 14–37% among men and 13–30% among women. The highest proportion for women was observed in Denmark, and for men in Navarra and Greece. In Italy, a large proportion of total carbohydrates was derived from ‘pasta, rice and other grain’ (men 19–24%, women 14–19%), compared with less than 10% in all other centres. Other important food subgroups contributing to total carbohydrate intake were fruits, cakes, dairy products, potatoes and ‘sugar, honey and jam’. Fruit consumption contributed to a higher proportion of total carbohydrates (mainly sugars not starches) in women than in men, and there was evidence of a north–south gradient, with the highest proportion in Spain and the lowest in Sweden and Norway. The proportion of total carbohydrates obtained from non-alcoholic beverages was highest in Germany and Norway (10–13%), particularly from fruit and vegetable juices, and also from carbonated drinks and diluted syrups in Norway (4–5%). Breakfast cereals represented less than 4% of total carbohydrates in all centres, except in the United Kingdom, where they contributed 6–9%.
Bread was an important source of total starch, ranging from 21% in Umeå to 62% in Navarra among men, and from 21% in Umeå to 58% in Aarhus among women. Crispbread was an important source of starch in Umeå (men 16%, women 14%), as was pasta in Italy (men 28–36%, women 22–32%). The contribution of potatoes to total starch varied from 4 to 5% in Italy for both genders, to a peak of 17% in Bilthoven for men and 16% in Asturias for women.
Fruits were a major source of total sugars, ranging from 12% in Umeå to 41% in Ragusa among men, and from 19% in Bilthoven, Sweden and Norway to 40% in Ragusa among women. Non-alcoholic beverages contributed about 20% of total sugars in Germany and Norway, and only about 5% in Greece, Spain and Southern Italy. Non-alcoholic beverages included fruit and vegetable juices, as well as soft drinks, and the contribution of these food sources to total sugars also varied considerably across countries; for example, in Copenhagen about 10% of total sugars was obtained from soft drinks and only about 5% from fruit and vegetable juices, whereas in Germany, about 15% of total sugars was obtained from fruit and vegetable juices and only about 5% from soft drinks. In northern centres, a high proportion of total sugars was also obtained from ‘sugar and confectionery’ (up to 29% in men in Bilthoven and 24% in women in Malmö), whereas the proportion was lowest for the UK health-conscious group (12% for men) and Murcia (13% for women).
Bread, fruits and vegetables represented the largest sources of fibre. The proportion of total fibre obtained from bread varied considerably across centres, from 16% among men in Umeå and 12% among women in Murcia to approximately 50% in Denmark for both men and women. The proportion of total fibre from fruit ranged from 12 to 34% in men and from 16 to 38% in women; the highest proportions were in Spain, France and Italy (especially Ragusa), and the lowest proportion was in the UK general population. Although vegetable intakes generally contributed less than 5% of total carbohydrates in men and women (except in Murcia and Greece), they contributed a relatively large proportion of total dietary fibre, especially in southern centres (up to 24% in men and 36% in women). Spain and Greece obtained a relatively high proportion of fibre from legumes, ranging from 9 to 19% in men and from 6 to 15% in women, compared with 0 to 6% for other centres.
Results are presented for total carbohydrate intakes for men and women, stratified by BMI, physical activity and smoking status (Tables 3a, 3b and 3c, respectively), after adjustment for age, energy, weight and height, and weighted for day of the week and season, as appropriate. In some centres (for example, Bilthoven), total carbohydrate intakes decreased with increasing BMI, but for most centres, there was little variation in carbohydrate intakes across BMI categories (Table 3a). There was a statistically significant trend towards higher carbohydrate intakes with increasing physical activity levels for men in Potsdam, Bilthoven and Copenhagen, and for women in Florence, Bilthoven and Aarhus (Table 3b). For several centres, we observed a lower carbohydrate consumption among current cigarette smokers compared with never-smokers for both men and women (Table 3c). In many centres, there was a statistically significant trend towards decreasing carbohydrate consumption with increasing alcohol intake. Carbohydrate intakes did not vary substantially with level of education. Stratified results for sugars, starch and fibre intakes are presented on the EPIC website (http://epic.iarc.fr).
There was a greater variation between centres in mean carbohydrate intakes on weekends (Friday–Sunday) than on weekdays (Monday–Thursday) for total carbohydrates (data not shown for sugars, starch and fibre). There was a statistically significant trend of decreasing total carbohydrate intakes between Monday and Sunday for men in Spain and Germany, and for women in Denmark, Norway, Greece and the Netherlands. This trend was particularly marked for fibre intake, which was significantly lower on weekends for most centres, except for the UK health-conscious group, which had peak fibre intake on weekends.
Overall, there was relatively little seasonal variation of total carbohydrate intakes (data not shown for sugars, starch and fibre). From spring through to winter, total carbohydrate intakes significantly increased for women in Italy, but decreased for women in the UK health-conscious population.
Comparable and detailed information on the foods contributing to dietary carbohydrate intakes across countries and populations is useful for guiding diet-related public health actions in the European Union, and for conducting and interpreting results of large multicentre nutritional studies. Some of the major obstacles for collecting and standardizing dietary assessments across different study populations and geographical regions, both at the food and nutrient level, include differences in the types of food available and in the methods of calculating food composition by means of country-specific nutrient databases. Using data from the ENDB, we minimized these obstacles by using standardized data collection methods and nutrient calculations. Thus, we have been able to describe and compare dietary carbohydrate component consumption across 10 European countries.
We observed a wide range of intakes and food sources of total carbohydrates, starch, sugars and fibre between centres. There was also evidence of different regional patterns, particularly between southern and northern centres. Given the cultural divergence and inherent regional differences between centres, it was expected that the predominant carbohydrate food sources would differ considerably. Although bread was the primary source of dietary carbohydrates in all centres, we observed large variations in the proportion of starch, sugar and fibre derived from these products, possibly related to a high inter-regional variability in bread types, ingredients and manufacturing techniques.
The proportion of total energy intake from dietary carbohydrates ranged from 35 to 50%, which is generally lower than the population nutrient intake goal of 50–75% recommended by the Food and Agriculture Organization and World Health Organization (Mann et al., 2007). Dietary guidelines also consistently emphasize the importance of consuming a high proportion of fibre-rich fruits, vegetables and whole grains, while limiting ‘free sugars’ (added sugars, plus sugars naturally present in honey, fruit juices and syrups) to less than 10% of total energy intake, as they may promote positive energy balance and weight gain without adding any micronutrients to the diet (Mazlan et al., 2006; Mann et al., 2007). The definition of (total) ‘sugars’ in the ENDB differs from the ‘free sugars’ definition, because it also includes lactose from dairy products. In spite of these recommendations, many countries are shifting towards higher energy-dense diets with more added sugars and reduced intakes of starchy foods, dietary fibre, fruits and vegetables (Stephen et al., 1995; Nishida and Martinez Nocito, 2007). This shift may be more prominent among younger generations and children, in contrast to the middle-aged and older participants recruited to the EPIC study (Marti-Henneberg et al., 1999).
We observed different carbohydrate intakes according to various lifestyle and personal factors, with a generally higher mean intake among physically active participants, among never-smokers, non-drinkers of alcohol and, to a lesser extent, among people with a BMI below 25 kg/m2. These findings did not differ greatly by region and are consistent with other cross-sectional studies (Dreon et al., 1988; Saris, 1989, 2003; Troisi et al., 1991).
Adjustment for total energy and anthropometric factors allowed us to explore whether differences in total carbohydrate intakes between groups were partly due to differences in total energy intake or in body size characteristics. Adjustment for total energy intake also helps to control for measurement errors in nutrient intake (Willett, 1998; Spiegelman, 2004), which might be more likely to occur in certain subgroups. For example, underestimation of energy intake is more common in overweight subjects and in certain EPIC centres (for example, Greece), whereas the San Sebastian centre had higher energy intakes, and thus the effect of energy adjustment on carbohydrate intakes differed for these centres (Ferrari et al., 2002, 2009; Slimani and Valsta, 2002c; Slimani et al., 2003). However, it is not known whether carbohydrate intakes are underreported to the same extent as total energy or other macronutrients, which could lead to selective (not neutral) reporting errors of certain nutrients (Lissner, 2002).
This is the largest study to date comparing carbohydrate intakes across several European countries. However, these findings may not be fully representative of the general population of each region, because not all EPIC populations were population based, and because of possible selective participation in those centres that were population based. When comparing our data with those of previous national surveys, total carbohydrate intakes as a proportion of dietary energy were similar in France (Volatier and Verger, 1999), Italy (Turrini et al., 1999) and Umeå (Becker, 1999), but were slightly lower in the EPIC UK general population (Henderson et al., 2003), in Danish EPIC centres (Haraldsdottir, 1999) and in the Malmö EPIC centre (Becker, 1999). In Norway, the intake of dietary fibre was 21 g/day in a 1993–94 national survey compared with 19 g/day in EPIC (Johansson et al., 1997). In Spain, the proportion of dietary energy from carbohydrates was slightly lower in EPIC participants than in nationally representative data (Moreno et al., 2002), perhaps reflecting a continuation of the decline in carbohydrate consumption observed in Spain between 1964 and 1991 (Moreno et al., 2002). Differences between EPIC and national data may also be due to different calculations of carbohydrate components (for example, in the Danish national surveys), sampling methods, type of dietary measurement, the age range of participants in these national surveys compared with EPIC and dietary changes over time.
Although dietary assessments were highly standardized, and nutrient intakes were calculated using harmonized nutrient databases, there were some small differences in the calculation of different carbohydrate components, which may have contributed to some variation in results. In addition, although we had data for several potential confounding factors, we were unable to examine or control for all factors that might influence dietary intake, such as socioeconomic status or factors inherent to the region or culture. Measurement error in reported carbohydrate intakes or other factors, for example, possible under-reporting of weight, may also have influenced results. Another limitation was that specific information regarding different types of dietary fibre, such as soluble and insoluble fibres, was not available, and thus only total fibre could be reported. These subtypes of fibre may have important differential effects on health and disease, although public health messages regarding total fibre intake may be more effective in informing the general public. Similarly, we could not separately determine the intake of wholegrain carbohydrates across all centres, although we were able to examine the food sources of fibres and other types of carbohydrates.
In this study, we measured diet simultaneously across 10 European countries, allowing comparison of carbohydrate intakes at a time of continual transition of dietary habits worldwide (Stephen et al., 1995). Dietary carbohydrate intakes and, in particular, their food sources varied considerably between these 10 European countries. Intakes also varied according to gender and lifestyle factors. These data will form the basis for future aetiological analyses of the roles of different types of dietary carbohydrates in influencing health and disease.
Supplementary information is available on the EPIC website (http://epic.iarc.fr).
Conflict of interest
CL Parr has received grant support from the Norwegian Foundation for Health and Rehabilitation. S Bingham has received grant support from MRC Centre. The remaining authors have declared no financial interests.
Augustin LS, Franceschi S, Jenkins DJ, Kendall CW, La Vecchia C (2002). Glycemic index in chronic disease: a review. Eur J Clin Nutr 56, 1049–1071.
Becker W (1999). Dietary guidelines and patterns of food and nutrient intake in Sweden. Br J Nutr 81 (Suppl 2), S113–S117.
Brustad M, Skeie G, Braaten T, Slimani N, Lund E (2003). Comparison of telephone vs face-to-face interviews in the assessment of dietary intake by the 24 h recall EPIC SOFT program-the Norwegian calibration study. Eur J Clin Nutr 57, 107–113.
Cust AE, Slimani N, Kaaks R, van Bakel M, Biessy C, Ferrari P et al. (2007). Dietary carbohydrates, glycemic index, glycemic load, and endometrial cancer risk within the European Prospective Investigation into Cancer and Nutrition Cohort. Am J Epidemiol 166, 912–923.
Daly M (2003). Sugars, insulin sensitivity, and the postprandial state. Am J Clin Nutr 78, S865–S872.
Daly ME, Vale C, Walker M, Alberti KG, Mathers JC (1997). Dietary carbohydrates and insulin sensitivity: a review of the evidence and clinical implications. Am J Clin Nutr 66, 1072–1085.
Deharveng G, Charrondiere UR, Slimani N, Southgate DA, Riboli E (1999). Comparison of nutrients in the food composition tables available in the nine European countries participating in EPIC. European Prospective Investigation into Cancer and Nutrition. Eur J Clin Nutr 53, 60–79.
DeVries JW, Rader JI (2005). Historical perspective as a guide for identifying and developing applicable methods for dietary fiber. J AOAC Int 88, 1349–1366.
Dreon DM, Frey-Hewitt B, Ellsworth N, Williams PT, Terry RB, Wood PD (1988). Dietary fat:carbohydrate ratio and obesity in middle-aged men. Am J Clin Nutr 47, 995–1000.
Englyst KN, Liu S, Englyst HN (2007). Nutritional characterization and measurement of dietary carbohydrates. Eur J Clin Nutr 61 (Suppl 1), S19–S39.
Ferrari P, Kaaks R, Fahey MT, Slimani N, Day NE, Pera G et al. (2004). Within- and between-cohort variation in measured macronutrient intakes, taking account of measurement errors, in the European Prospective Investigation into Cancer and Nutrition study. Am J Epidemiol 160, 814–822.
Ferrari P, Roddam A, Fahey MT, Jenab M, Bamia C, Ocké M et al. (2009). A bivariate measurement error model for nitrogen and potassium intakes to evaluate the performance of regression calibration in the European Prospective Investigation into Cancer and Nutrition study. Eur J Clin Nutr 63 (Suppl 4), S179–S187.
Ferrari P, Slimani N, Ciampi A, Trichopoulou A, Naska A, Lauria C et al. (2002). Evaluation of under- and overreporting of energy intake in the 24-hour diet recalls in the European Prospective Investigation into Cancer and Nutrition (EPIC). Public Health Nutr 5 (Suppl), S1329–S1345.
Haftenberger M, Lahmann PH, Panico S, Gonzalez CA, Seidell JC, Boeing H et al. (2002a). Overweight, obesity and fat distribution in 50- to 64-year-old participants in the European Prospective Investigation into Cancer and Nutrition (EPIC). Public Health Nutr 5 (Suppl), S1147–S1162.
Haftenberger M, Schuit AJ, Tormo MJ, Boeing H, Wareham N, Bueno-de-Mesquita HB et al. (2002b). Physical activity of subjects aged 50–64 years involved in the European Prospective Investigation into Cancer and Nutrition (EPIC). Public Health Nutr 5 (Suppl), S1163–S1176.
Haraldsdottir J (1999). Dietary guidelines and patterns of intake in Denmark. Br J Nutr 81 (Suppl 2), S43–S48.
Henderson L, Gregory J, Irving K, Swan G (2003). The National Diet & Nutrition Survey: Adults Aged 19 to 64 years: Energy, Protein, Carbohydrate, Fat and Alcohol Intake 2, Stationery Office: London.
Holt SH, Miller JC, Petocz P (1997). An insulin index of foods: the insulin demand generated by 1000-kJ portions of common foods. Am J Clin Nutr 66, 1264–1276.
Hu FB, van Dam RM, Liu S (2001). Diet and risk of type II diabetes: the role of types of fat and carbohydrate. Diabetologia 44, 805–817.
Johansson L, Solvoll K, Bjorneboe GEA, Drevon CA (1997). Dietary habits among Norwegian men and women. Scand J Nutr 41, 63–70.
Kaaks R, Plummer M, Riboli E, Esteve J, van Staveren W (1994). Adjustment for bias due to errors in exposure assessments in multicenter cohort studies on diet and cancer: a calibration approach. Am J Clin Nutr 59, S245–S250.
Kaaks R, Riboli E, van Staveren W (1995). Calibration of dietary intake measurements in prospective cohort studies. Am J Epidemiol 142, 548–556.
Key TJ, Spencer EA (2007). Carbohydrates and cancer: an overview of the epidemiological evidence. Eur J Clin Nutr 61 (Suppl 1), S112–S121.
Lissner L (2002). Measuring food intake in studies of obesity. Public Health Nutr 5, 889–892.
Mann J, Cummings JH, Englyst HN, Key T, Liu S, Riccardi G et al. (2007). FAO/WHO scientific update on carbohydrates in human nutrition: conclusions. Eur J Clin Nutr 61 (Suppl 1), S132–S137.
Marti-Henneberg C, Capdevila F, Arija V, Perez S, Cuco G, Vizmanos B et al. (1999). Energy density of the diet, food volume and energy intake by age and sex in a healthy population. Eur J Clin Nutr 53, 421–428.
Mazlan N, Horgan G, Whybrow S, Stubbs J (2006). Effects of increasing increments of fat- and sugar-rich snacks in the diet on energy and macronutrient intake in lean and overweight men. Br J Nutr 96, 596–606.
Meyer KA, Kushi LH, Jacobs Jr DR, Slavin J, Sellers TA, Folsom AR (2000). Carbohydrates, dietary fiber, and incident type 2 diabetes in older women. Am J Clin Nutr 71, 921–930.
Moreno LA, Sarria A, Popkin BM (2002). The nutrition transition in Spain: a European Mediterranean country. Eur J Clin Nutr 56, 992–1003.
Nishida C, Martinez Nocito F (2007). FAO/WHO scientific update on carbohydrates in human nutrition: introduction. Eur J Clin Nutr 61 (Suppl 1), S1–S4.
Ocké MC, Larrañaga N, Grioni S, van den Berg SW, Ferrari P, Salvini S et al. (2009). Energy intake and sources of energy intake in the European Prospective Investigation into Cancer and Nutrition. Eur J Clin Nutr 63 (Suppl 4), S3–S15.
Riboli E, Hunt KJ, Slimani N, Ferrari P, Norat T, Fahey M et al. (2002). European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr 5 (Suppl), S1113–S1124.
Rimm EB, Ascherio A, Giovannucci E, Spiegelman D, Stampfer MJ, Willett WC (1996). Vegetable, fruit, and cereal fiber intake and risk of coronary heart disease among men. JAMA 275, 447–451.
Saris WH (1989). Physiological aspects of exercise in weight cycling. Am J Clin Nutr 49, 1099–1104.
Saris WH (2003). Sugars, energy metabolism, and body weight control. Am J Clin Nutr 78, S850–S857.
Selwitz RH, Ismail AI, Pitts NB (2007). Dental caries. Lancet 369, 51–59.
Slimani N, Bingham S, Runswick S, Ferrari P, Day NE, Welch AA et al. (2003). Group level validation of protein intakes estimated by 24-hour diet recall and dietary questionnaires against 24-hour urinary nitrogen in the European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study. Cancer Epidemiol Biomarkers Prev 12, 784–795.
Slimani N, Deharveng G, Charrondiere RU, van Kappel AL, Ocke MC, Welch A et al. (1999). Structure of the standardized computerized 24-h diet recall interview used as reference method in the 22 centers participating in the EPIC project. European Prospective Investigation into Cancer and Nutrition. Comput Methods Programs Biomed 58, 251–266.
Slimani N, Deharveng G, Unwin I, Southgate DA, Vignat J, Skeie G et al. (2007). The EPIC nutrient database project (ENDB): a first attempt to standardize nutrient databases across the 10 European countries participating in the EPIC study. Eur J Clin Nutr 61, 1037–1056.
Slimani N, Fahey M, Welch AA, Wirfalt E, Stripp C, Bergstrom E et al. (2002a). Diversity of dietary patterns observed in the European Prospective Investigation into Cancer and Nutrition (EPIC) project. Public Health Nutr 5 (Suppl), S1311–S1328.
Slimani N, Ferrari P, Ocke M, Welch A, Boeing H, Liere M et al. (2000). Standardization of the 24-hour diet recall calibration method used in the European Prospective Investigation into Cancer and Nutrition (EPIC): general concepts and preliminary results. Eur J Clin Nutr 54, 900–917.
Slimani N, Kaaks R, Ferrari P, Casagrande C, Clavel-Chapelon F, Lotze G et al. (2002b). European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study: rationale, design and population characteristics. Public Health Nutr 5, 1125–1145.
Slimani N, Valsta L, EFCOSUM Group (2002c). Perspectives of using the EPIC-SOFT programme in the context of pan-European nutritional monitoring surveys: methodological and practical implications. Eur J Clin Nutr 56, S63–S74.
Smith U (1994). Carbohydrates, fat, and insulin action. Am J Clin Nutr 59, S686–S689.
Spiegelman D (2004). Commentary: correlated errors and energy adjustment-where are the data? Int J Epidemiol 33, 1387–1388.
Stephen AM, Sieber GM, Gerster YA, Morgan DR (1995). Intake of carbohydrate and its components-international comparisons, trends over time, and effects of changing to low-fat diets. Am J Clin Nutr 62, S851–S867.
Troisi RJ, Heinold JW, Vokonas PS, Weiss ST (1991). Cigarette smoking, dietary intake, and physical activity: effects on body fat distribution-the Normative Aging Study. Am J Clin Nutr 53, 1104–1111.
Turrini A, Leclercq C, D’Amicis A (1999). Patterns of food and nutrient intakes in Italy and their application to the development of food-based dietary guidelines. Br J Nutr 81 (Suppl 2), S83–S89.
van Dam RM, Seidell JC (2007). Carbohydrate intake and obesity. Eur J Clin Nutr 61 (Suppl 1), S75–S99.
Volatier JL, Verger P (1999). Recent national French food and nutrient intake data. Br J Nutr 81 (Suppl 2), S57–S59.
Willett W (1998). Nutritional Epidemiology, 2nd edn. Oxford University Press: New York.
Wirfalt E, McTaggart A, Pala V, Gullberg B, Frasca G, Panico S et al. (2002). Food sources of carbohydrates in a European cohort of adults. Public Health Nutr 5, 1197–1215.
This work was carried out with the financial support of the European Commission: Public Health and Consumer Protection Directorate 1993–2004; Research Directorate-General 2005; Ligue contre le Cancer (France); Société 3M (France); Mutuelle Générale de l’Education Nationale; Institut National de la Santé et de la Recherche Médicale (INSERM); Institut Gustave Roussy; German Cancer Aid; German Cancer Research Center; German Federal Ministry of Education and Research; Danish Cancer Society; Health Research Fund (FIS) of the Spanish Ministry of Health; Spanish Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra and the Catalan Institute of Oncology; and ISCIII RETIC (RD06/0020), Spain; Cancer Research UK; Medical Research Council, UK; the Stroke Association, UK; British Heart Foundation; Department of Health, UK; Food Standards Agency, UK; the Wellcome Trust, UK; Greek Ministry of Health; Hellenic Health Foundation; Italian Association for Research on Cancer; Italian National Research Council, Regione Sicilia (Sicilian government); Associazione Iblea per la Ricerca Epidemiologica—ONLUS (Hyblean association for epidemiological research, NPO); Dutch Ministry of Health, Welfare and Sport; Dutch Prevention Funds; LK Research Funds; Dutch ZON (Zorg Onderzoek Nederland); World Cancer Research Fund (WCRF); Swedish Cancer Society; Swedish Research Council; Regional Government of Skane and the County Council of Vasterbotten, Sweden; Norwegian Cancer Society; the Norwegian Research Council and the Norwegian Foundation for Health and Rehabilitation. Anne Cust received a PhD scholarship from the University of Sydney, a Research Scholar Award from the Cancer Institute NSW, and a NHMRC Public Health Research Fellowship (520018), Australia. We thank Sarah Somerville, Nicole Suty and Karima Abdedayem for their assistance with editing, Kimberley Bouckaert and Heinz Freisling for their technical assistance, and Carine Biessy for statistical support.
Guarantor: AE Cust.
Contributions: AEC carried out the statistical analysis, preparation of tables and figures, and wrote the paper, taking into account comments from all co-authors. NS was the overall coordinator of this project and of the EPIC nutritional databases (ENDB) project. MRS, MvB, JH, AO, CA, TP, EB, ES, MDC were members of the writing group and gave input on the statistical analysis, drafting of the manuscript and interpretation of results. The other EPIC authors were local EPIC collaborators involved in the collection of dietary and other data, and in the ENDB project. ER is the overall coordinator of the EPIC study. All co-authors provided comments and suggestions on the manuscript and approved the final version.
About this article
Meal analysis for understanding eating behavior: meal- and participant-specific predictors for the variance in energy and macronutrient intake
Nutrition Journal (2019)
Short‐Term Isocaloric Intake of a Fructose‐ but not Glucose‐Rich Diet Affects Bacterial Endotoxin Concentrations and Markers of Metabolic Health in Normal Weight Healthy Subjects
Molecular Nutrition & Food Research (2019)
Validity and Reproducibility of a Self-Administered Food Frequency Questionnaire for the Assessment of Sugar Intake in Middle-Aged Japanese Adults
Extrinsic wheat fibre consumption enhances faecal bulk and stool frequency; a randomized controlled trial
Food & Function (2019)
Banana starch and molecular shear fragmentation dramatically increase structurally driven slowly digestible starch in fully gelatinized bread crumb
Food Chemistry (2019)