Energy intake and sources of energy intake in the European Prospective Investigation into Cancer and Nutrition



To describe energy intake and its macronutrient and food sources among 27 regions in 10 countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study.


Between 1995 and 2000, 36 034 subjects aged 35–74 years were administered a standardized 24-h dietary recall. Intakes of macronutrients (g/day) and energy (kcal/day) were estimated using standardized national nutrient databases. Mean intakes were weighted by season and day of the week and were adjusted for age, height and weight, after stratification by gender. Extreme low- and high-energy reporters were identified using Goldberg's cutoff points (ratio of energy intake and estimated basal metabolic rate <0.88 or >2.72), and their effects on macronutrient and energy intakes were studied.


Low-energy reporting was more prevalent in women than in men. The exclusion of extreme-energy reporters substantially lowered the EPIC-wide range in mean energy intake from 2196–2877 to 2309–2866 kcal among men. For women, these ranges were 1659–2070 and 1873–2108 kcal. There was no north–south gradient in energy intake or in the prevalence of low-energy reporting. In most centres, cereals and cereal products were the largest contributors to energy intake. The food groups meat, dairy products and fats and oils were also important energy sources. In many centres, the highest mean energy intakes were observed on Saturdays.


These data highlight and quantify the variations and similarities in energy intake and sources of energy intake among 10 European countries. The prevalence of low-energy reporting indicates that the study of energy intake is hampered by the problem of underreporting.


Nowadays, in Europe, an enormously rich variety of foods is available on the market, and this very abundance, especially of energy-dense foods and drinks, is considered to be one of the factors leading to energy intakes higher than individual biological and physiological requirements (Swinburn et al., 2004). People whose energy intake is high in comparison with their energy expenditure gain weight and ultimately develop overweight or even obesity. Thus, together with a low level of physical activity, dietary energy intake is of major importance in the aetiology of obesity (Swinburn et al., 2004; Branca et al., 2007).

Dietary monitoring in Europe is organized at the national level and is not standardized across countries (Brussaard et al., 2002; Elmadfa and Weichselbaum, 2005). For this reason, there are no comparable data on energy intakes across Europe, apart from some studies on specific populations (de Groot et al., 1999). Moreover, energy intake is difficult to measure. The doubly-labelled water method, the only golden standard method, cannot be applied to large-scale studies because of its high cost and sophisticated laboratory requirements (Livingstone and Black, 2003). Other methods that rely on self-reporting of food consumption may suffer from systematic underreporting (Kipnis et al., 2003).

In the European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study, highly standardized 24-h dietary recalls (24-HDR) were collected from almost 37 000 participants randomly selected from among 27 regions in 10 European countries (Slimani et al., 2002). In addition, a harmonized nutrient database was compiled (Slimani et al., 2007), which allows a reliable comparison of energy intakes between these countries. To provide advice to policy makers and evaluate dietary policies with regard to overweight and obesity, it is important that nationally representative and comparable data on energy intake (and expenditure) of good quality become available for all European countries. Although the study populations of the EPIC cohort study are not nationally representative samples of the European general populations, results from the EPIC calibration study may identify important differences in energy intakes and profiles across Europe.

In this descriptive paper, we examine variations in energy intake among 27 regions, in different population subgroups and by day of the week. In addition, the relative contributions of macronutrients and various food groups to energy intake are presented.

Materials and methods

Study population

The EPIC calibration study was nested within the European Prospective Investigation into Cancer and Nutrition, a multi-centre cohort study aimed at investigating the association between diet, cancer and other chronic diseases across 10 European countries: Denmark, France, Germany, Greece, Italy, the Netherlands, Norway, Spain, Sweden and the United Kingdom (Riboli et al., 2002; Slimani et al., 2002). EPIC participants were mostly recruited from the general population residing within defined geographical areas, with some exceptions: women members of a health insurance for school employees (France); women attending breast cancer screening (Utrecht, the Netherlands); blood donors (some centres in Italy and Spain) and a cohort consisting predominantly of vegetarians (‘health-conscious’ cohort in Oxford, UK). In Norway, participants from the entire country were included (Slimani et al., 2002). The original 23 administrative EPIC centres were reclassified into 27 regions according to a geographical south–north gradient. Nineteen of the 27 EPIC regions had both female and male participants, and eight recruited only women: regions belonging to France, Norway, Utrecht (the Netherlands) and Naples (Italy). Individual habitual dietary intake was assessed using different questionnaires in each country (Riboli et al., 2002). The calibration study was undertaken between 1995 and 2000 to express individual dietary intakes according to the same reference scale and to partially correct diet–disease associations for attenuation due to measurement errors (Ferrari et al., 2004). The calibration population sample consisted of 36 994 participants, that is, an 8% stratified random sample of the total EPIC cohort.

A total of 36 034 subjects with 24-HDR data were included in this analysis, after exclusion of 960 subjects aged under 35 or over 74 years because of low participation 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

Previous publications outline in detail the rationale, methodology and population characteristics of the 24-HDR calibration study (Kaaks et al., 1994, 1995; Slimani et al., 2002; Ferrari et al., 2008). In brief, each participant provided a single 24-HDR in a face-to-face interview, except in Norway, where it was obtained by telephone interview (Brustad et al., 2003). A computerized interview programme, named EPIC-SOFT, was developed to conduct highly standardized 24-HDR interviews (Slimani et al., 1999, 2000). The interviews were distributed over various seasons and over different days of the week. In most countries, for logistical constraint reasons, interviews with regard to diet on Saturday were conducted on Monday, whereas for all other days of the week, the interview was conducted the following day. The classification of the EPIC-SOFT food (sub-) groups used in the calibration study is derived from a system described in detail elsewhere (Slimani et al., 2002).

Energy intakes (kcal/day) and contributions of total carbohydrates, fat, protein and alcohol were estimated from the 24-HDR using country-specific food composition tables that were standardized across countries to allow comparisons at the nutrient level. The EPIC Nutrient Database (ENDB) project outlines in detail the methods used to standardize the national nutrient databases across the 10 countries, including matching of EPIC foods to the national databases, deriving nutrient values of unavailable foods and imputing missing values (Slimani et al., 2007). The energy content of a food was calculated as the sum of the factored contributions from standardized protein, carbohydrates, fat and alcohol using the Atwater factors in kJ 17, 17, 37 and 29 (in kcal 4, 4, 9 and 7) per gram of protein, carbohydrate, fat and alcohol, respectively.

The ratio of reported energy intake (EI) to estimated basal metabolic rate (BMR) based on Schofield equations (Schofield, 1985) was used to ascertain the magnitude of misreporting. Goldberg's cutoff points for a single day in populations with a sedentary lifestyle, physical activity level (PAL) of 1.55 (Goldberg et al., 1991), were used to identify participants with a physiologically extreme-energy intake (low-energy reporter=EI/BMR<0.88 and high-energy reporter=EI/BMR>2.72) as outlined previously (Ferrari et al., 2002).

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 (Haftenberger et al., 2002; Riboli et al., 2002; Slimani et al., 2002). Data on age as well as 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., 2002).

Statistical methods

Data are presented as mean intakes and s.e., stratified by study centre and gender. Mean energy intakes were adjusted for age (except when stratified by age), height and weight and were 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 effect of exclusion of extreme-energy reporters and of not adjusting for height and weight on mean energy intakes and centre rankings.

The contribution of macronutrients and food groups to total energy intakes was calculated and expressed as energy percent (E%). For food subgroups, the contribution of energy intake from the subgroup to energy intake from the main food group was calculated. These analyses were adjusted for age but not for height and weight because we wanted to describe the actual composition of the diet in European populations. The effects of exclusion of extreme-energy intake were examined because misreporting may be more pronounced for certain macronutrients (Livingstone and Black, 2003).

We also performed stratified analyses to determine differences in energy intake according to age (10-year age groups), body mass index (BMI) category (<25; 25–30; >30 kg/m2), smoking status (never; ex; current), education level (none or primary school; technical/professional/secondary school; university), category of physical activity (inactive; moderately inactive; moderately active; active), season (quarters of the year) and day of the week. Stratification for physical activity could not be performed for Umeå and for the Norwegian cohorts, because their data were not comparable. In stratified analyses, we retained age, weight and height in the models in addition to weighting by season and day of the week. The stratified analyses were performed by country rather than by centre to prevent many cells with small numbers. As the health-conscious cohort in the United Kingdom is so different from the general population cohort, these two groups were kept separate.

Analyses were performed using SAS (version 9.1, SAS Institute, Cary, NC, USA).


Mean intakes of energy

After adjustment for age, height and weight and weighting by season and day of 24-HDR, centre-specific mean reported energy intakes ranged from 2196 to 2877 kcal/day among men and from 1659 to 2070 kcal/day among women (Table 1). There was no north–south gradient in energy intake. In about one in four male cohorts and one in seven female cohorts, energy intake decreased with age, whereas in Varese (Italy), an increase with age was observed among men. Without adjustment for weight and height, mean energy intakes in the southern European centres were about 20–100 kcal (that is, 1–6%) lower, whereas in most other centres, mean intakes were higher (up to 70 kcal; that is, 0–3%) (Table A1 in Appendix). Additional adjustment for physical activity (not possible for Norway and for Umeå, Sweden) had a minor impact on centre-specific mean energy intakes: the changes ranged from a decrease of 18 kcal to an increase of 30 kcal/day, which is about 1% of total mean energy intake (data not shown).

Table 1 Fully adjusteda mean daily intakes of total energy in kcal by centre ordered from south to north, gender, and age group

Low-energy reporting was more prevalent in women than in men (Table 2). Among male participants, the percentage of low-energy reporters (EI/BMR ratio <0.88) ranged from 4% in San Sebastian (Spain) to 18% in Greece. Among women, low-energy reporting ranged from 6% in northwest France to 32% in Greece. Low-energy reporting was more prevalent among the 6835 participants who were following a special diet (19% of the study population). In subjects not on a special diet, low-energy reporting ranged from 0 to 16% in men and from 4 to 29% in women (data not shown). The percentage of high-energy reporters (EI/BMR >2.72) was low in both genders, with the highest percentages (4–5%) observed in Ragusa (Italy). There was no north–south gradient in extreme-energy reporting. After exclusion of the 5211 extreme-energy reporters (14.5%), age-, height- and weight-adjusted mean energy intakes changed considerably for some centres (Table 2 vs Table 1). With some exceptions among men, mean energy intake generally increased, illustrating the significantly larger proportion of low-energy reporters compared with high-energy reporters. For men, the average change was an increase of 3% in centre-specific energy intakes and the largest impact of about 170 kcal (7%) was seen in Malmö and Greece. For men, after exclusion of extreme-energy reporters, the lowest mean daily energy intake of 2309 kcal was still observed in the UK health-conscious cohort and the highest still in San Sebastian (2866 kcal/day). For women, exclusion of extreme-energy reporters resulted in an average increase of 5% in centre-specific mean energy intakes. France was the only country in which the increase in all centres was less than 5%. In contrast, the largest change of about 215 kcal (13%) was observed in Greek women. As a consequence, the mean daily energy intake among Greek women became similar to the energy intake among women in Granada (Spain) and in the UK general population cohort. For women, the range in energy intake became 1873–2108 after exclusion of extreme-energy reporters. After exclusion of extreme-energy reporters, the maximum difference in mean adjusted energy intake across centres decreased from about 680 to 560 kcal in men, and from 410 to 240 kcal in women (Table 2 vs Table 1).

Table 2 Percentage of extreme-energy reportersa, low-energy reportersb and high-energy reportersc and adjustedd mean energy intakes in kcal after exclusion of extreme-energy reporters by gender and centre ordered from south to north

Contributions of macronutrient intake to total energy intake

Table 3 presents the age-adjusted mean contributions of macronutrient intake to total energy intake on the basis of the whole study population. The highest mean proportions of fat intake were observed in the Greek cohort (men 40.7 E%, women 42.2 E%). The lowest proportions of fat were observed in Italy; for men it was <30 E% in all Italian centres and for women it was <33 E% in three of the Italian centres, and also in the UK general population cohort. Centres with a high mean contribution of energy from fat generally had a low contribution from carbohydrates and vice versa (correlation coefficient −0.8). The range in the mean contribution of carbohydrate intake to total energy intake was 35–50 E%. In the UK health-conscious cohort, the highest mean E% of carbohydrates (50 E%) and the lowest E% of protein (13 E%) were observed in both men and women. In other centres, the mean protein intake ranged between 14 and 21 E%. In men, there seemed to be a north–south gradient; the mean contribution of protein to energy intake was 16 E% in centres in Mediterranean countries, but it was lower in other centres. Such a gradient was less clear in women. The mean contribution of alcohol to energy intake was highest in Copenhagen (men 9.2 E%, women 6.9 E%). The lowest contribution of alcohol was observed in Umeå for men (2.5 E%) and in Granada for women (1.1 E%). In all centres, the average contribution of alcohol to energy intake was higher in men than in women.

Table 3 Minimally adjusteda mean daily intakes of total fat, total carbohydrates, protein and alcohol as a percentage of total daily energy intake by gender and centre ordered from south to north

Exclusion of extreme-energy reporters gave slightly different results (Data not shown but available on the EPIC website ( The mean contributions of fat and alcohol increased slightly (average increase in fat of 0.2 E% for men and 0.5 E% for women over centres, and an average increase in alcohol of 0.1 E% for both genders), whereas the contributions of carbohydrates and protein decreased slightly (average changes in carbohydrates over centres: men −0.2 E%, women −0.3 E%; average changes for protein over centres: men −0.2 E%, women −0.4 E%).

Contributions of food groups to total energy intake

The age-adjusted proportions of total energy intake contributed by the EPIC-SOFT food groups stratified by gender are shown in Figures 1a and b. The tables related to these figures are given on the EPIC website ( The distribution of energy intake within subgroups of the main EPIC-SOFT food groups is described in the text, where relevant.

Figure 1

Sources of energy intake (E%) in (a) men and (b) women, adjusted for age and weighted for day of 24-h recall and season, by centre ordered from south to north.

In almost all centres, cereals and cereal products made the largest contribution to energy intake, but the proportion varied considerably, ranging from 14.7 to 34.3 E% in men and from 14.0 to 30.4 E% in women (Figure 1). In all Italian centres, cereals contributed to more than one-quarter of energy intake. This was also the case for men in the Greek and UK health-conscious cohorts. Of all the energy provided by the cereal group, bread contributed 75% in Greece and 50–60% in Italy and in the UK health-conscious group; pasta and rice contributed 30–45% in the Italian centres and 15% in the Greek and health-conscious cohorts; and breakfast cereals contributed 20% in the health-conscious cohort and a negligible proportion in Greece and Italy (data not shown). The lowest proportions of cereals were observed in some of the Spanish centres (Figure 1).

The proportion of total energy intake from dairy products was smallest in the UK health-conscious cohort (men 6.2 E%, women 8.9 E%), whereas the highest contributions were observed in men in Umeå (14.4 E%) and in women in Utrecht (17.5 E%) (Figure 1). Among women, dairy products contributed more than 15% to total energy intake in French and Norwegian cohorts, in Asturias and Granada (Spain), and in Utrecht (the Netherlands) and Umeå (Sweden). In all centres, the percentages of energy intake from dairy products were higher in women than in men.

The proportion of energy intake from meat in the UK health-conscience cohort (<2 E%) was much lower than that in other EPIC centres because of the high proportion of vegans, ovo-lacto vegetarians and fish eaters (Figure 1). In other cohorts, the proportion of energy from meat varied from 6.5% in Greek women to 17.5% in San Sebastian men (Spain). In three Spanish cohorts, San Sebastian, Asturias and Navarra and in both German cohorts, meat contributed 15% to men's total energy intake. In most cohorts, the contribution of meat to energy intake was lower for women than for men.

In Greek men and women, the food group fats and oils contributed one-fifth to total energy intake, which is higher than in any other centre (Figure 1). In some Spanish centres, the proportion was >15 E% (both genders in Navarra, women in Granada and Murcia). This was also the case for men in Potsdam, where the proportion of energy from the food group fats and oils (15.4 E%) was considerably higher than that in the other German cohort in Heidelberg (9.0 E%). In the southern European centres with a high-energy contribution from fats and oils, this was mainly derived from vegetable oils, whereas in Potsdam, butter and margarine contributed the most. In contrast, the lowest percentage of energy intake from the food group fats and oils was observed in Varese (6.8 E%) among men, and in Norway and Aarhus (6–7 E%) among women (Figure 1).

In all cohorts, the contribution of cakes to energy intake was higher in women than in men (Figure 1). Cakes contributed >10% to total energy intake in both genders in the UK cohorts, and in women in Murcia, Utrecht, Sweden, Aarhus and in the Italian centres except Turin. The lowest proportion of energy intake from cakes was observed in Greek men.

Furthermore, for fruit, the contribution to energy intake was greater in women than in men. Contributions <5% were observed only among men in cohorts of the UK general population, Sweden and Denmark. Contributions >10 E% were observed in the UK health-conscious cohort, in Murcia and, among women only, in Granada. In contrast to Murcia and Granada, energy provided by the food group ‘fruit’ in the UK health-conscious cohort included more energy from nuts (spreads) and seeds (38%) (data not shown).

Men had, however, a higher contribution to energy intake from alcoholic beverages than did women (Figure 1). For men in Copenhagen and Heidelberg, the proportion was 11 E%. Among women, the highest contributions from alcoholic beverages were also observed in these centres, together with Aarhus, but in the range of 6–8 E%.

Stratified analyses

Total mean energy intakes by country, for men and women, stratified by day of the week, are presented in Table 4. These data are adjusted for age, height and weight and weighted by season. In most but not all countries, mean energy intake was highest on Saturday, followed by Friday or Sunday. Even when alcohol was excluded, the highest mean energy intakes were observed on Saturdays in most countries (data not shown).

Table 4 Fully adjusteda mean daily energy intakes (s.e.) in kcal by country and gender, according to day of the week

For stratifications by level of physical activity, no significant increases in adjusted mean energy intake with higher level of physical activity were observed across the cohorts. Similarly, no consistent trends in adjusted mean energy intakes were observed across levels of education, BMI category, smoking status and season (data not shown).


In this study, a wide range of mean energy intakes was observed between the centres of the EPIC study, with higher average values for men than for women. After correction for differences in age, height and weight between the study populations and standardization for differences in distribution of interviews over days of the week and seasons, the maximum difference between centre mean energy intakes amounted to almost 700 kcal/day for men and to more than 400 kcal/day for women. However, a substantial part of this variation was due to differences in low-energy reporting. The prevalence of low-energy reporting was larger for women than for men.

Underreporting of energy intake is a phenomenon frequently observed in Western adult populations. It occurs in all kinds of studies that use dietary assessment methods that rely on self-reported dietary intake (Livingstone and Black, 2003). We evaluated underreporting at the individual level against presumed energy requirements, assuming a PAL of 1.55 appropriate for moderately active populations (Haftenberger et al., 2002), taking into account the uncertainty of recalling a single day's diet. This approach has several limitations. First, a PAL of 1.55 is a conservative value and may therefore underestimate the number of underreporters. Second, the cutoff identifies low-energy reporters, and has low sensitivity and poor specificity for identifying true underreporters. Persons who underreport from a higher intake in such a way that EI/BMR does not fall below the cutoff for PAL of 1.55*BMR are not identified (Livingstone and Black, 2003). Moreover, some of the low-energy reporters may truly be consuming a very low-energy diet. In this study, this seems more likely for participants following a special diet.

The observed low-energy reporting confirms previously published results using energy contents of foods from country-specific food composition tables rather than the ENDB (Ferrari et al., 2002). Many studies applying presumed energy requirements to identify underreporters observed more prevalent low-energy reporting among women than among men. It is unclear, however, whether underreporting is also more prevalent among women or whether this is because of the application of a single cutoff for EI/BMR. Doubly-labelled water studies suggest that men have a higher physical activity level and thus higher energy intake. A single cutoff would then identify fewer men as underreporters (Livingstone and Black, 2003).

A previous analysis of the EPIC calibration dataset showed that persons on a special diet and overweight people were significantly more likely to be low-energy reporters than were normal weight subjects and those not on a special diet (Ferrari et al., 2002). A higher prevalence of low-energy reporters among people with a higher BMI has been observed in many studies (Heitmann and Lissner, 1995; Braam et al., 1998; Livingstone and Black, 2003). In addition, low-energy reporting might be related to age, as well as to socioeconomic, psychosocial and behavioural characteristics (Livingstone and Black, 2003; Maurer et al., 2006).

The aim of the EPIC calibration study is to express dietary measurements, obtained using country- and centre-specific dietary assessment methods, on a common scale and to correct for bias because of measurement errors in dietary assessment. It is assumed that the 24-HDRs used are unbiased or have equal bias across centres. This study confirms the previous observation (Ferrari et al., 2002) that underreporting differs between centres, with the highest level in the Greek cohort (men 18%, women 32% versus 4–15% for men and 6–22% for women in other centres). This provides evidence regarding the limitations of the use of 24-HDRs as reference measurements in the calibration study (Ferrari et al., 2008). Ferrari et al. showed that, after exclusion of extreme-energy reporters, the EI/BMR ratio was fairly constant. The question is whether a solution would be found to exclude the 24-HDRs of low-energy reporters when calibrating dietary intake in the EPIC study. This might also introduce unknown biases in the dataset.

The problem of underreporting is also relevant when nutrients are the topic of interest, as underreporting of energy intake is associated with underreporting of nutrient intake. It is known that the degree of underreporting differs by nutrients (Livingstone and Black, 2003). The data of this study also suggest that underreporting was larger for fat and alcohol than for protein and carbohydrate intake. With regard to potassium and nitrogen, measurement errors in the EPIC calibration study have been investigated using urinary excretion data. The results suggested that 24-HDRs can be used as reference measurements at individual and aggregate levels for potassium intake. For nitrogen intake, performance was good for between–centre calibration (Slimani et al., 2003), but some limitations were apparent at the individual level (Ferrari et al., 2009). Adjustment for energy intake seems to solve a major part of the impact of measurement error in nutrient intake (Kipnis et al., 2003). However, because low-energy reporting is also related to personal characteristics (for example, BMI) that may determine disease outcome, exclusion of low-energy reporters and adjustment for energy intake in the case of nutrients do not solve the whole problem. More complex measurement error models are needed that will account for systematic bias (for example, depending on BMI) as well as random errors.

Regional differences in sources of energy intake were more pronounced than were variations in energy intake. This was expected, as physiological variations in energy intakes should be relatively modest after adjustment for age, gender, physical activity and anthropometry (Black and Cole, 2000). Greater variations in energy intake were observed in men than in women.

As expected, the UK health-conscious cohort showed a very specific pattern of energy sources. In comparison with other centres, meat and dairy products contributed only small proportions to total energy intake, whereas the food groups ‘fruits’ (which includes nuts and seeds) and ‘miscellaneous’ (which includes soy products) contributed relatively large proportions. As in the Italian and Greek cohorts, cereals contributed to more than a quarter of total daily energy intake in the UK health-conscious cohort.

The highest contributions of meat were observed in the Spanish and German cohorts, and some of the lowest were observed in Italy. Dairy products made relatively large contributions to energy intake in the Dutch, French, Swedish and Norwegian cohorts and also in some of the Spanish centres. The highest contributions from the food group fats and oils were observed in the Greek and Spanish cohorts and in Potsdam, Germany. The type of fat differed between these Mediterranean countries and Potsdam, as has been described in more detail by Linseisen et al. (2009).

The contributions of macronutrients to energy intake also varied considerably across the European centres. There was a difference of 10 E% in mean fat intake between the Greek and Italian centres. Only among Italian men was the average fat intake in accordance with the international recommendation of <30 E% (WHO/FAO, 2003). The range in the mean contribution of carbohydrates to total energy intake was 35–50 E%. Centres with a high mean contribution of energy from fat in general had a low contribution from carbohydrates and vice versa. The mean protein intake ranged between 13 and 21 E%. In men, the mean contribution of protein to energy intake was higher in the Mediterranean centres than in the more northern centres. The mean contribution of alcohol to energy intake was highest in Copenhagen (men 9.2 E%, women 6.9 E%). In all centres, men had a higher average contribution of alcohol to energy intake than did women. In general, these results are in line with the results from national food consumption surveys such as those summarized in the European Nutrition and Health Report 2004 (Elmadfa and Weichselbaum, 2005). A detailed comparison of our results and those of the European Nutrition and Health Report would be inappropriate because of differences in dietary assessment methods and populations. Further details regarding energy intake from fat, including various types of fatty acids, carbohydrates, protein and alcohol are described elsewhere in this Supplement (Cust et al., 2009; Halkjær et al., 2009; Linseisen et al., 2009; Sieri et al., 2009). These papers also present results on absolute intakes of macronutrients, their determinants and food sources.

In most of the EPIC cohorts, highest mean energy intakes were observed on and around Saturdays, even when the energy contribution of alcohol intake was excluded. Although expected, little evidence of this weekday variation exists in literature. In addition, in some of the cohorts, an inverse association between energy intake and age was observed. For other sociodemographic and lifestyle variables, including physical activity level and season, no consistent associations were present in the data. For physical activity, this is surprising, as higher physical activity should be accompanied by higher energy intake to maintain energy balance. In a previous multivariate analysis of covariance within the EPIC calibration study, physical activity at work and during leisure time was a predictor of EI/BMR (Ferrari et al., 2002).

This is the largest study to date describing intake of energy and its sources across several European countries. One of the strengths of this descriptive paper is the use of a standardized dietary assessment methodology (EPIC-SOFT programme) as well as a standardized food composition table, the ENDB (Slimani et al., 2007), making it possible to compare intake of energy and macronutrients across 10 countries and 27 regions. Comparable and detailed information on energy intake and sources of energy intake across countries is useful for conducting and interpreting the results of large multi-centre dietary studies. Furthermore, the large geographical span makes it possible to study the manner in which different food patterns across Europe contribute to energy intake.

However, as not all EPIC cohorts are population based, the results cannot be extrapolated to the general population of each region. Another limitation is that each participant provided only one 24-HDR. Intake can therefore be evaluated only for group means rather than at the individual level. This makes it impossible to estimate the percentage of the population that adheres to dietary recommendations for the contribution of various macronutrients to energy intake.

In conclusion, we measured diet in a highly standardized manner across 10 European countries. Our findings highlight and quantify the variations and similarities in energy intake and its sources between these countries. This information is important for future aetiological analyses on how energy intake and different types of macronutrients are related to health outcomes. Moreover, the presence of underreporting of energy and nutrient intake in the reported diets should be taken into account in analyses on diet in relation to health and disease.

Supplementary information

Supplementary information is available on the EPIC website (

Conflict of interest

M Jenab has received grant support from the World Cancer Research Fund. 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.


  1. Black AE, Cole TJ (2000). Within- and between-subject variation in energy expenditure measured by the doubly-labelled water technique: implications for validating reported dietary energy intake. Eur J Clin Nutr 54, 386–394.

    CAS  Article  Google Scholar 

  2. Braam LA, Ocke MC, Bueno-de-Mesquita HB, Seidell JC (1998). Determinants of obesity-related underreporting of energy intake. Am J Epidemiol 147, 1081–1086.

    CAS  Article  Google Scholar 

  3. Branca F, Nikogosian H, Lobstein T (eds). (2007). The Challenge of Obesity in the WHO European Region and the Strategies for Response. WHO Europe: Copenhagen.

    Google Scholar 

  4. Brussaard JH, Johansson L, Kearney J (2002). Rationale and methods of the EFCOSUM project. Eur J Clin Nutr 56 (Suppl 2), S4–S7.

    Article  Google Scholar 

  5. 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.

    CAS  Article  Google Scholar 

  6. Cust AE, Skilton MR, van Bakel MME, Halkjær J, Olsen A, Agnoli C et al. (2009). Total dietary carbohydrate, sugar, starch and fibre intakes in the European Prospective Investigation into Cancer and Nutrition. Eur J Clin Nutr 63 (Suppl 4), S37–S60.

    CAS  Article  Google Scholar 

  7. de Groot CP, van den Broek T, van Staveren W (1999). Energy intake and micronutrient intake in elderly Europeans: seeking the minimum requirement in the SENECA study. Age Ageing 28, 469–474.

    CAS  Article  Google Scholar 

  8. Elmadfa I, Weichselbaum E (eds) (2005). European Nutrition and Health Report 2004. Forum Nutrition, vol. 58. Karger: Basel.

    Google Scholar 

  9. Ferrari P, Day NE, Boshuizen HC, Roddam A, Hoffmann K, Thiebaut A et al. (2008). The evaluation of the diet/disease relation in the EPIC study: considerations for the calibration and the disease models. Int J Epidemiol 37, 368–378.

    Article  Google Scholar 

  10. 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.

    Article  Google Scholar 

  11. 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.

    CAS  Article  Google Scholar 

  12. 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-h diet recalls in the European Prospective Investigation into Cancer and Nutrition (EPIC). Public Health Nutr 5 (Suppl), S1329–S1345.

    Article  Google Scholar 

  13. Goldberg GR, Black AE, Jebb SA, Cole TJ, Murgatroyd PR, Coward WA et al. (1991). Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur J Clin Nutr 45, 569–581.

    CAS  PubMed  Google Scholar 

  14. Haftenberger M, Schuit AJ, Tormo MJ, Boeing H, Wareham N, Bueno-de-Mesquita HB et al. (2002). 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.

    Article  Google Scholar 

  15. Halkjær J, Olsen A, Bjerregaard LJ, Deharveng G, Tjønneland A, Welch AA et al. (2009). Intake of total, animal and plant proteins and their food sources in 10 countries in the European Prospective Investigation into Cancer and Nutrition. Eur J Clin Nutr 63 (Suppl 4), S16–S36.

    Article  Google Scholar 

  16. Heitmann BL, Lissner L (1995). Dietary underreporting by obese individuals—is it specific or non-specific? Br Med J 311, 986–989.

    CAS  Article  Google Scholar 

  17. Kaaks R, Plummer M, Riboli E, Esteve J, van Staveren WA (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.

    Article  Google Scholar 

  18. Kaaks R, Riboli E, van Staveren WA (1995). Calibration of dietary intake measurements in prospective cohort studies. Am J Epidemiol 142, 548–556.

    CAS  Article  Google Scholar 

  19. Kipnis V, Subar AF, Midthune D, Freedman LS, Ballard-Barbash R, Troiano RP et al. (2003). Structure of dietary measurement error: results of the OPEN biomarker study. Am J Epidemiol 158, 14–21; discussion 22–26.

    Article  Google Scholar 

  20. Linseisen J, Welch AA, Ocké M, Amiano P, Agnoli C, Ferrari P et al. (2009). Dietary fat intake in the European Prospective Investigation into Cancer and Nutrition: results from the 24-h dietary recalls. Eur J Clin Nutr 63 (Suppl 4), S61–S80.

    CAS  Article  Google Scholar 

  21. Livingstone MB, Black AE (2003). Markers of the validity of reported energy intake. J Nutr 133 (Suppl 3), S895–S920.

    Article  Google Scholar 

  22. Maurer J, Taren DL, Teixeira PJ, Thomson CA, Lohman TG, Going SB et al. (2006). The psychosocial and behavioral characteristics related to energy misreporting. Nutr Rev 64, 53–66.

    Article  Google Scholar 

  23. 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.

    Article  Google Scholar 

  24. Schofield WN (1985). Predicting basal metabolic rate; new standards and review of previous work. Hum Nutr Clin Nutr 39 (Suppl 1), S5–S41.

    Google Scholar 

  25. Sieri S, Krogh V, Saieva C, Grobbee DE, Bergmann M, Rohrmann S et al. (2009). Alcohol consumption patterns, diet and body weight in 10 European countries. Eur J Clin Nutr 63 (Suppl 4), S81–S100.

    CAS  Article  Google Scholar 

  26. 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.

    PubMed  Google Scholar 

  27. Slimani N, Deharveng G, Charrondière RU, van Kappel AL, Ocké 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. Comput Methods Programs Biomed 58, 251–266.

    CAS  Article  Google Scholar 

  28. 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.

    CAS  Article  Google Scholar 

  29. Slimani N, Ferrari P, Ocke M, Welch A, Boeing H, van Liere M et al. (2000). Standardization of the 24-h 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.

    CAS  Article  Google Scholar 

  30. Slimani N, Kaaks R, Ferrari P, Casagrande C, Clavel-Chapelon F, Lotze G et al. (2002). European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study: rationale, design and population characteristics. Public Health Nutr 5 (Suppl), S1125–S1145.

    Article  Google Scholar 

  31. Swinburn BA, Caterson I, Seidell JC, James WP (2004). Diet, nutrition and the prevention of excess weight gain and obesity. Public Health Nutr 7, 123–146.

    CAS  PubMed  Google Scholar 

  32. WHO/FAO (2003). Diet, Nutrition and the Prevention of Chronic Diseases (Report of a Joint WHO/FAO Expert Consultation). WHO Technical Report 916. World Health Organization: Geneva.

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The work described in this paper was carried out with the financial support of the European Commission: Public Health and Consumer Protection Directorate 1993–2004; of the 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. We thank Sarah Somerville, Nicole Suty and Karima Abdedayem for assistance with editing and Kimberley Bouckaert and Heinz Freisling for technical assistance.

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Correspondence to M C Ocké.

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Guarantor: MC Ocké.Contributors: MO carried out statistical analysis, prepared 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. MO, NS, NL, SG, SB, PF, SS, VB, JL and EW were members of the writing group and gave input on statistical analysis, drafting of the paper and interpretation of results. The other co-authors were local EPIC collaborators involved in the collection of data, and in documenting, compiling and evaluating the subset of their national nutrient databases used in the ENDB. ER is the overall coordinator of the EPIC study. All co-authors provided comments and suggestions on the paper and approved the final version.



Table A1

Table a1 Minimally adjusteda mean daily intakes of total energy (kcal) by centre ordered from south to north, gender and age group

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Ocké, M., Larrañaga, N., Grioni, S. et al. Energy intake and sources of energy intake in the European Prospective Investigation into Cancer and Nutrition. Eur J Clin Nutr 63, S3–S15 (2009).

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  • energy intake
  • underreporting
  • dietary fat
  • 24-h dietary recall
  • Europe
  • EPIC-soft

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