Original Article

European Journal of Clinical Nutrition (2009) 63, S61–S80; doi:10.1038/ejcn.2009.75

Dietary fat intake in the European Prospective Investigation into Cancer and Nutrition: results from the 24-h dietary recalls

Guarantor: J Linseisen.

Contributors: JL carried out the statistical analyses, prepared the tables and figures, and wrote the paper, taking into account comments from all co-authors. NS was the overall coordinator of this project and the EPIC nutritional databases (ENDB) project. AW, MO, PA, CA, PF, ES, VC, and HB B-d-M were members of the ‘working group on fat intake’ and gave inputs on the statistical analysis, drafting of the paper and interpretation of the results. RK, CW, MD, LR, IE, AM, YTvdS, JM, SN, MJ, EL, MB, JH, MUJ, KTK, FC, CG, GM, MN, MT, SB 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.

J Linseisen1,27, A A Welch2,28, M Ocké3, P Amiano4, C Agnoli5, P Ferrari6,29, E Sonestedt7, V Chajès6,8, H B Bueno-de-Mesquita3, R Kaaks1, C Weikert9, M Dorronsoro4, L Rodríguez10, I Ermini11, A Mattiello12, Y T van der Schouw13, J Manjer14, S Nilsson15, M Jenab16, E Lund17, M Brustad17, J Halkjær18, M U Jakobsen19, K T Khaw20, F Crowe21, C Georgila22, G Misirli22, M Niravong23, M Touvier23,24, S Bingham2,25, E Riboli26 and N Slimani6

  1. 1Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
  2. 2Department of Public Health and Primary Care, MRC Centre for Nutritional Epidemiology in Cancer Prevention and Survival, University of Cambridge, Cambridge, UK
  3. 3National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
  4. 4Public Health Department of Gipuzkoa, Basque Government, San Sebastian and CIBER Epidemiología y Salud Pública (CIBERESP), Spain
  5. 5Department of Preventive & Predictive Medicine, Nutritional Epidemiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
  6. 6Dietary Exposure Assessment Group, International Agency for Research on Cancer, Lyon, France
  7. 7Department of Clinical Sciences, Lund University, Malmö, Sweden
  8. 8Institut Gustave Roussy, CNRS FRE 2939, Villejuif, France
  9. 9Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbrücke, Germany
  10. 10Public Health and Participation Directorate, Health and Health Care Services Council, Asturias, Spain
  11. 11Molecular and Nutritional Epidemiology Unit, ISPO, Florence, Italy
  12. 12Department of Clinical and Experimental Medicine, University of Naples, Federico II, Naples, Italy
  13. 13Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands
  14. 14Department of Surgery, Malmö University Hospital, Malmö, Sweden
  15. 15Department of Nutritional Research, University of Umeå, Umeå, Sweden
  16. 16Lifestyle and Cancer Group, International Agency for Research on Cancer, Lyon, France
  17. 17Institute of Community Medicine, University of Tromsø, Tromsø, Norway
  18. 18Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen, Denmark
  19. 19Department of Clinical Epidemiology, Aalborg Hospital, Aarhus University Hospital, Aalborg, Denmark
  20. 20University of Cambridge School of Clinical Medicine, Addenbrookes Hospital, Cambridge, UK
  21. 21Cancer Epidemiology Unit, University of Oxford, Oxford, UK
  22. 22Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece
  23. 23Inserm, ERI 20, Institut Gustave Roussy, Villejuif, France
  24. 24AFSSA (French Food Safety Agency), DERNS/PASER, Maisons-Alfort, France
  25. 25Diet and Cancer Group, MRC Mitochondrial Biology Unit, Cambridge, UK
  26. 26Department of Epidemiology, Public Health and Primary Care, Imperial College, London, UK

Correspondence: Dr J Linseisen, Helmholtz Centre Munich, Institute of Epidemiology, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany. E-mail: j.linseisen@helmholtz-muenchen.de

The author is deceased.

27Current address: Institute of Epidemiology, Helmholtz Centre Munich, Neuherberg, Germany.

28Current address: School of Medicine, Health Policy and Practice, University of East Anglia, Norwich, UK.

29Current address: Data Collection and Exposure Unit (DATEX), European Food Safety Authority, Parma, Italy.





This paper describes the dietary intake of total fat, saturated (SFA), monounsaturated (MUFA) and polyunsaturated fatty acids (PUFA) and cholesterol of participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) in 27 centres across 10 countries.



Between 1995 and 2000, a stratified random sample of 36034 participants (age range 35–74 years) completed a standardized 24-h dietary recall, assessed by means of the computer software EPIC-SOFT. Lipid intake data were calculated using a standardized nutrient database.



On average, the contribution of fat to total energy intake was greater than or equal to34% of energy intake (%en) in women and greater than or equal to36%en in men for most EPIC centres, except for the British, Dutch and most Italian cohorts. Total fat (>40%en) and MUFA intakes (21%en, mainly from olive oil) were highest in Greece. Except for the Greek, Spanish and Italian centres, the average MUFA intake ranged between 10 and 13%en, with a high proportion derived from animal sources. SFA intake in women and men was lowest in the Greek, Spanish, Italian and UK cohorts with an average of less than or equal to13%en (down to 9%en), and highest in the Swedish centres (16%en). The mean PUFA intake was in the range of 4–8%en, being highest in the UK health-conscious cohort. The average cholesterol intake across EPIC varied from 140 to 384mg/d in women and 215–583mg/d in men.



The presented data show differences and similarities in lipid intake across the European EPIC cohorts and also show differences in food sources of dietary lipids.


EPIC, 24-h diet recalls, dietary intake, lipids, EPIC-Soft, ENDB



Diet has a major impact on modulating the risk and severity of a number of chronic diseases including obesity and obesity-related metabolic disorders, cardiovascular diseases and cancer. Among macronutrients, dietary fat has been studied extensively in recent decades, and both the quantity and quality of dietary fat intake have been considered. Dietary advice for reducing cardiovascular risk includes the limitation of total fat, saturated (SFA), cholesterol and trans fatty acid intake, whereas the intake of n-3 polyunsaturated fatty acids (PUFA) (fish oil fatty acids) has been reported to exert beneficial effects (Mead et al., 2006; Brunner et al., 2007). Despite improvements in pharmacological treatment, modification of lipid intake (restriction of saturated fat, trans fatty acids and cholesterol intake) is still recommended for controlling dyslipidaemia, especially high plasma LDL cholesterol (Grundy, 2007). In line with the results of many short-term intervention studies, lower total fat intake was shown to be associated with a lower body weight over 7 years of intervention in the WHI trial (Howard et al., 2006). As reflected by current discussions on the suggested effect of fat intake (total fat, SFA, trans fatty acids) on breast cancer risk (Bingham and Day, 2006; Thiébaut et al., 2007; Chajès et al., 2008), the role of dietary lipids in cancer prevention still needs to be defined (WCRF/AICR, 2007).

The promotion of dietary recommendations on the quantity and quality of dietary fat intake has increased public awareness of possible health risks because of a high or unbalanced lipid intake and, along with modifications to the lipid content of processed foods by the food industry (for example, low-fat foods), lipid intake may have changed over time (Helsing, 1993). Thus, it is desirable to have more precise data on lipid intake, ensuring comparability across European countries, and this can be achieved through standardized food composition databases. So far, the most comprehensive cross-European data on lipid supply have been based either on household budget survey data (DAFNE—food availability at the household level) or on a compilation of intake data based on different dietary assessment methods as reported in the European Nutrition and Health Report 2004 (Elmadfa and Weichselbaum, 2005). In both instances, country-specific food composition tables were applied to derive nutrient intake estimates.

This study evaluates dietary intake data as assessed by means of standardized 24-h diet recalls in a representative subgroup of each cohort participating in the European Prospective Investigation into Cancer and Nutrition (EPIC), an international multi-centre cohort study primarily aimed at studying relationships between diet and the development of chronic diseases, particularly cancer (Riboli and Kaaks, 1997; Slimani et al., 2002a). The recently created EPIC Nutrient Database (ENDB) (Slimani et al., 2007), which harmonizes separate nutrient databases from 10 European countries, now makes it possible to calculate dietary lipid intake data with improved comparability across EPIC centres. Previous analyses of EPIC food group intake data revealed significant differences between centres, for example, for the consumption of added fats and oils, meat and meat products and dairy products (Hjartåker et al., 2002; Linseisen et al., 2002a, 2002b), which could also result in differences in fat intake. Following this work, we present here the results of a detailed analysis of the intake of dietary fat, types of fatty acids and cholesterol across the EPIC centres.


Materials and methods

Study population

The study population sample consisted of a stratified random sample (36994 women and men) from the cohorts participating in EPIC, who were administered a standardized, computer-assisted 24-h dietary recall (24-HDR) (Slimani et al., 2002a). This calibration study was conducted between 1995 and 2000 to improve the comparability of food frequency-derived dietary data across EPIC countries and centres by partially correcting for dietary measurement error arising from country- or centre-specific bias and random and systematic within-person errors (Willett, 1998; Ferrari et al., 2004). The EPIC cohorts were recruited in 10 western European countries (Greece, Spain, Italy, France, Germany, the Netherlands, United Kingdom, Denmark, Sweden and Norway) to investigate the associations between diet, lifestyle and chronic diseases, especially cancer (Riboli et al., 2002; Slimani et al., 2002a). 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 (centres in Italy and Spain) and a cohort consisting predominantly of vegetarians (‘health-conscious’ cohort in Oxford, UK) (Riboli et al., 2002). Nineteen of the 27 EPIC centres had both female and male participants, and eight centres recruited only women (France, Norway, Utrecht and Naples).

A total of 36034 subjects with 24-HDR data were included in this analysis, after systematic 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 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 (Brustad et al., 2003). A computerized interview programme (EPIC-SOFT) was developed specifically for the calibration study (Slimani et al., 1999, 2000). A detailed description of the rationale, methodology and population characteristics of the 24-h recall calibration study nested in the EPIC cohort is given elsewhere (Kaaks et al., 1994, 1995; Slimani et al., 2002a).

Dietary intakes (g/d) of total fat, types of fatty acids and cholesterol were estimated from the 24-HDR using country-specific food composition tables that were standardized as far as possible across countries to allow calibration 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 EPIC foods to the national databases, deriving nutrient values of unavailable foods and imputing missing values (Slimani et al., 2007). The definitions of total fat (including the glycerol moiety), SFA, MUFA, PUFA and cholesterol and the methods used to determine their values have been described earlier (Slimani et al., 2007). As the standardization of individual fatty acids was not performed because of the lack of reliable local data, a distinction between n-6 and n-3 PUFA was not possible. However, the available data on the food source of fat allowed a description of fat intake by consumption of foods of plant origin, animal origin or mixed/unspecified origin. An ‘ORIGIN’ variable has been given by the compilers for each national item used in EPIC to compile the ENDB. It gives only qualitative information on the predominant animal and/or plant origin of the food (‘100% animal origin’, ‘above 95% animal origin’, ‘100% plant origin’, ‘above 95% plant origin’, ‘mixed origin’, ‘non-organic’, ‘unknown’). We endeavoured to use the variable ‘ORIGIN’ to obtain quantitative information on fat intake; approximations of 5% error were accepted (‘above 95%’). Otherwise, the fat origin was coded as ‘unknown’. On the basis of this information, it was possible to estimate the intake of fat of animal and plant origin. Where the origin was unclear (as, for example, for ready-to-eat dishes and cakes without any clear declaration or containing ingredients of mixed or unknown origin), fat origin was classified as ‘unknown’.

In addition, information on major food sources contributing to the intake of total fat, SFA, MUFA, PUFA and cholesterol is provided, using the refined EPIC-SOFT food classification scheme.

Data on other lifestyle factors, including the educational level, total physical activity and smoking history considered in this analysis, were collected at baseline through standardized questionnaires and clinical examinations for the calibration sample, and have been described elsewhere (Riboli et al., 2002; Slimani et al., 2002a; Haftenberger et al., 2002a, 2002b). Data on age as well as body weight and height were self-reported by the 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., 2002a).

Statistical methods

Dietary intake data are presented as mean (M, least square mean) and standard error (s.e.), stratified by gender, study centre and 10-year age groups and ordered according to a geographical south/north gradient.

In generalized linear models, the mean intake data were adjusted for age, and weighted by season and day of the week of 24-HDR to control for different distributions of 24-HDR interviews across seasons and days of the week (Tables A1–A5 in Appendix). Such minimally adjusted intake data are given in all articles of this supplement and ensure direct comparability across articles. However, the rest of the analyses were performed using the fully adjusted model; that is, adjusted further for total energy, body weight and height. (Tables 1, 2, 3, 4, 5, 6 and 7).

We examined the effect of adjustment for several covariates—including total energy intake, body weight and height, BMI, smoking status, education level and physical activity—on the mean intake data of total fat, SFA, MUFA, PUFA and cholesterol. Analyses were run stratified by BMI, smoking status, educational level, physical activity and season (data not shown but available on the EPIC website (http://epic.iarc.fr)). Fat intake data were presented in g/d and in percentage contribution to total daily energy intake (%en). If fewer than 20 persons were represented in a stratum defined by centre, gender and age group, descriptive data are not presented in the tables.

The contribution of food groups to total fat, SFA, MUFA, PUFA and cholesterol intake is given as the mean percentage of intake (percentage of total intake, derived from the crude intake data); the contribution of a subgroup is given as a percentage of the food group. The contribution of food groups to total fat is provided in Table 8. The contribution for the other fat components is not shown but is available on the EPIC website (http://epic.iarc.fr). The categorization into food groups and food subgroups is common across centres and is adapted from the EPIC-SOFT food classification system as described elsewhere (Slimani et al., 2000, 2002b). All statistical analyses were performed using SAS software (version 9.1, SAS Institute, Cary, NC, USA).



Minimally adjusted data on fat, fatty acid and cholesterol intake (Tables A1, Tables A2, Tables A3, A4 and A5) are presented in the Appendix. Total energy intake proved to be by far the strongest predictor of variability, whereas the other tested covariates, including smoking, BMI, physical activity and education, explained only a very small part, if any, of the variation (data not shown but available on the EPIC website (http://epic.iarc.fr)). Thus, the following section presents data adjusted for age, total energy intake (not for data expressed as %en), body weight and height and weighted by season and day of the week of the recall.

Total fat intake

In both men and women, fully adjusted mean total fat intake was lowest in Turin with 77.7 and 65.6g/d, and highest in Greece with 113.9 and 87.3g/d, respectively (Table 1). Expressed as a contribution to the total energy intake, the corresponding figures were 28.3%en (men) and 31.3%en (women) in Turin and 40.9%en (men) and 42.0%en (women) in Greece (Table 6). A mean total fat intake of greater than or equal to36%en in men and greater than or equal to34%en in women was found in the majority of EPIC centres except for the UK, the Netherlands, Asturias/Spain (men) and most centres in Italy (Figures 1a and b). Differences by gender were statistically significant at P<0.001. In many EPIC centres, fat intake decreased with age; lower mean intake values were noted especially in the highest age group (65–74 years) (Table A1). After adjustment for energy intake, the differences became smaller or disappeared (Table 1). In southern European centres, about half of the total fat intake (Italian centres, Asturias/Spain) or more than half (Greece, most Spanish centres) was of plant origin (Table 7). In central and northern European centres, food of animal origin was the dominant fat source. Especially in the Scandinavian centres, consumption of mixed margarines containing animal and plant fat in varying amounts contributed to the group ‘fat of mixed or unknown origin’. This pattern is also reflected in the contribution of specific food groups and subgroups to the total fat intake by centre, as listed in Table 8.

Figure 1.
Figure 1 - Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author

Mean intake of total fat, saturated (SFA), monounsaturated (MUFA) and polyunsaturated fatty acids (PUFA) (as a percentage of total energy intake) in (a) men and (b) women in the EPIC cohorts (adjusted for age, weight, height and weighted by day of 24-HDR and season). (Note: lines between centre means are included to facilitate readability of the graphs, but they do not indicate a relationship between centre means.)

Full figure and legend (302K)

Intake of SFA, MUFA and PUFA

On average, SFA intake in men and women was lowest in the Greek, Spanish, Italian and UK cohorts with an average intake of less than or equal to13%en (down to 9%en) (Table 6), which corresponds to less than or equal to35g/d in men and less than or equal to28g/d in women (Table 2). In the other EPIC countries/centres (except for men in Bilthoven), SFA intake was greater than or equal to14%en in both men and women (Figures 1a and b). Mean intake figures were highest in both Swedish centres (16%en). Again, differences by gender were statistically significant (P<0.001). The contribution of food groups to SFA intake by EPIC centre is not shown but is available on the EPIC website (http://epic.iarc.fr), the main sources being dairy products, meat, added fats and oils and cakes.

The adjusted mean intake of MUFA among men ranged from 30g/d in Bilthoven (the Netherlands) to 56g/d in Greece (Table 3). Among women, the corresponding figures were 21g/d (Dutch centres) and 42g/d (Greece). In Greek women and men, MUFA intake provided more than 20% of the total energy intake. Except for the Greek, Spanish and most Italian centres, average MUFA intake was between 10%en and 13%en (Figures 1a and b). Food sources of MUFA also differed in a similar manner: in Greece, Spain and Italy, vegetable (olive) oil provided more than 40% (up to 64% in Greece) of MUFA intake, whereas in most other EPIC centres the main contributors to total MUFA intake were meat and meat products, added fats and dairy products (data not shown but available on the EPIC website (http://epic.iarc.fr); the UK health-conscious cohort differed from the other centres, with an expected low contribution from meat and meat products but a high contribution from nuts and seeds to overall MUFA (and to a minor extent also to SFA and PUFA) intake. Although statistically significant, differences among gender were generally small.

The mean PUFA intake among women ranged between 9 and 16g/d (Table 4), corresponding to 4–7%en (Table 6). Among men, the mean intake figures were between 10 and 21g/d, or 4–8%en. The highest PUFA intake was noted for the UK health-conscious cohort, with a contribution from nuts and seeds of >15% of the total intake (data not shown but available on the EPIC website: http://epic.iarc.fr).

Cholesterol intake

Cholesterol intake was lowest in the UK health-conscious cohort, with mean intake figures of 215mg in men and 140mg in women, followed by the Greek and Dutch centres (Table 5). The highest average cholesterol intakes—with up to 583mg in men and 384mg in women in San Sebastian—were found for the northern Spanish cohorts (San Sebastian, Asturias and Navarra). As expected, the main food sources of cholesterol were meat, eggs, dairy products, fish and cakes (data not shown but available on the EPIC website (http://epic.iarc.fr). A specifically high contribution of butter to total cholesterol intake was observed for the German centres, followed by the UK general population.



Using the recently standardized food composition tables (ENDB) (Slimani et al., 2007), we were able to improve the comparability of the fat content data among 10 European countries. The standardized dietary assessment instrument applied in all cohorts—24-HDRs administered with the software EPIC-SOFT—also ensured the validity of the data on fat intake across the EPIC centres. We observed a wide range of intake of total fat, types of fatty acids and cholesterol in EPIC at the group level. Despite some similarities in the average lipid intake among several EPIC centres, marked differences in lipid intake profiles were observed, particularly between the Mediterranean and other EPIC centres.

As could be expected from food-based analyses in the EPIC cohorts (Linseisen et al., 2002a, 2002b), Greek participants had the highest total fat and MUFA intake in EPIC, provided mainly by olive oil. Other southern European centres (Spain followed by Italy) showed a high MUFA intake but, at the same time, Italian centres had the lowest intake of total fat and SFA. Thus, southern European centres are characterized by high consumption of olive oil—a rich source of MUFA—and consequently by levels of MUFA in excess of SFA, whereas overall lipid intake in central and northern Europe (with the exception of the UK health-conscious cohort) is typified by a high proportion of lipids of animal origin—a rich source of saturated fats—and thus by levels of SFA higher than MUFA. SFA intakes in UK women and men from the general population cohort were intermediate between the results obtained for the southern European cohorts on the one hand and for the French, German and Scandinavian cohorts on the other hand. The UK health-conscious cohort also showed a very specific lipid intake pattern, with relatively low intakes of SFA and cholesterol but the highest intake of PUFA. Average cholesterol intake was also low in the Dutch and Greek EPIC centres.

The main sources of total fat intake were similar across EPIC but varied substantially in terms of percent contribution to intake. More than 50% (up to 76% in Greece) of the total fat intake was provided by the three food groups: ‘added fats and oils’ (including also fats and oils used during food preparation), ‘meat and meat products’ and ‘dairy products’. An exception is the UK health-conscious cohort with only 41% (women) and 45% (men) of total fat from these three groups, and with lipids derived from ‘nuts and seeds’ contributing as much as 10% to the total lipid intake. The food group ‘cakes’ represented the fourth most important group in terms of its contribution to total fat intake, with an average of about 10% (range 4–15%). Even in countries/centres with a higher consumption of fatty fish (Welch et al., 2002) the contribution of fish to total fat intake was relatively small (less than or equal to5%). In the Spanish centres, fat from eggs provided up to 5% of fat intake. The high egg consumption combined with high meat and meat product consumption (Linseisen et al., 2002b) helps to understand the comparatively high cholesterol intake in most Spanish centres.

Lipid intake data in EPIC and data obtained in the underlying populations, that is, from national or regional representative nutrition surveys, cannot be compared directly. It should be borne in mind that even the population-based EPIC cohorts are not strictly representative of the underlying population (Boeing et al., 1999; Riboli et al., 2002) because only volunteers can be enrolled, thus limiting external validity. Weighting for the deviation from the underlying population, as done for national evaluations (Linseisen et al., 2003), was not performed for the current analyses because the data provided are meant to describe the lipid intake of the EPIC cohorts as a tool for investigating associations with disease risk. The European Nutrition and Health Report 2004 (Elmadfa and Weichselbaum, 2005) provides a comprehensive listing of dietary intake data assessed during roughly the same time window as the EPIC 24-HDRs. Data were included from adults in 14 European countries, covering all EPIC countries except the Netherlands. The dietary assessment methods applied in the different countries were, however, extremely heterogeneous, and unstandardized national food composition tables were used for nutrient calculations. Nevertheless, our findings are largely consistent with those reported from studies using more limited databases and procedures, but more representative samples (Elmadfa and Weichselbaum, 2005).

We then evaluated the mean intake levels in the EPIC cohorts in the light of dietary guidelines on lipid intake. Such guidelines may provide goals at the individual or at the population level, a difference that may have important implications for their interpretation. As we are dealing with data from a single dietary recall per subject, we can only present data at the group level. The proportion of total energy intake from fat ranged from 31 to 42%en in women and 28 to 41%en in men, which is in most cases (except for Italian men) higher than the goal of <30%en recommended by international and national expert panels (Sandström et al., 1996; DGE, 2000; EURODIET, 2000; WHO/FAO, 2003). In many EPIC cohorts, the average total fat intake even exceeded the recommended dietary allowance of up to 35%en as set by the UK and US scientific boards (Department of Health, 1991; Food and Nutrition Board, 2005). Except for most Italian and Spanish EPIC centres and men in the UK health-conscious cohort, in which intakes were on average close to the SFA intake recommendation of less than or equal to10%en (Department of Health, 1991; Sandström et al., 1996; DGE, 2000; EURODIET, 2000; WHO/FAO, 2003), all other EPIC centres exceeded SFA intake recommendations. Concerning PUFA intake, an acceptable range of intake is given as 4–8%en (EURODIET, 2000) or 5–10%en (Sandström et al., 1996; Food and Nutrition Board, 2005); an intake of greater than or equal to3%en (n-6 PUFA: 2.5%en; n-3 PUFA: 0.5%en) has also been recommended by a European expert group (DGE, 2000). The mean PUFA intake in the EPIC cohorts was between 4 and 8%en, and was thus in the acceptable range. Among men, the average cholesterol intake in most centres exceeded the recommended intake of <300mg/d (DGE, 2000; WHO/FAO, 2003; Food and Nutrition Board, 2005), except for Greece, Bilthoven and the UK health-conscious cohort. However, among women, the mean cholesterol intake was below or close to 300mg/d in almost all centres; only women in the northern Spanish centres had distinctly higher levels.

As we observed a very strong association between total energy intake and lipid intake data, we present only energy-adjusted data (g/d, %en) in the article, whereas non-energy-adjusted data are provided in the appendix. Although the levels of intake unadjusted for energy intake better reflect absolute intake levels, adjustment for total energy intake takes care of part of the measurement errors included in nutrient intake data (Willett, 1998; Spiegelman, 2004); it also takes into account the large physiological differences in anthropometry and physical activity reported between centres. It is well documented that overweight subjects are more likely to underestimate energy intake than normal weight subjects (Ferrari et al., 2002). In addition, in the EPIC study, we already observed that participants in Greece were more likely than those in other EPIC countries to underreport total energy intake (Ferrari et al., 2002). Energy adjustment, however, also had a considerable effect on mean intake data in some other centres, including men in San Sebastian, Varese, the UK health-conscious cohort and Aarhus and women in some French centres and Aarhus. Our data consistently showed significant differences by gender for all lipid intake data (g/d) investigated. In addition, owing to the large size of the cohort, after adjustment for energy intake (data in %en), gender differences were still statistically significant even though actual differences decreased, for example, for MUFA intake.

Besides total energy intake and gender, we observed no distinct, consistent within- and between-centre effects of other factors (including education, physical activity, BMI, smoking, season) on the lipid intake results. Analysis of variance often showed statistically significant associations, but comparisons between strata of the potential covariates (including P for trend) showed no clear patterns (of practical relevance). Socio-economic status can obviously influence dietary habits (Lallukka et al., 2007), but this may not necessarily be reflected in differences in lipid intake (Giskes et al., 2004). In our study, education as a proxy of socio-economic status explained only a small part of the variation in lipid intake data. Smoking has also been reported to be related to diet quality (Boynton et al., 2008), but we identified no substantial impact on fat intake in EPIC. The same is true for BMI or physical activity level (information available on the EPIC website: http://epic.iarc.fr).

A limitation of this study is the missing distinction between n-6 and n-3 PUFA, which is because of lack of information and standardization of individual fatty acid data across national food composition tables. Alternatively, plasma phospholipid fatty acid composition as a biomarker of fatty acid intake can be used to describe differences in (long-chain) n-6 and n-3 PUFA supply, an approach followed in a subsample of our study and detailed elsewhere (Saadatian-Elahi et al., 2009). In this work, we used a food-based approach to distinguish between lipids of plant versus those of animal origin. Although plant-derived lipids were the dominating source of fat intake in the southern European countries—Greece, Spain and Italy, as well as in the UK health-conscious cohort—fats of animal origin clearly dominated in France (women) and Germany (see information available on the EPIC website (http://epic.iarc.fr)). In the other central or more northern European centres (the Netherlands, the UK general population, Denmark, Sweden and Norway), where the contribution of mixed fats (that is, mixed margarines, consisting of fat of animal and plant origin) was more important, the distinction between plant and animal sources of fat intake became less clear. The differences in consumption of fat of animal origin closely follow differences in arachidonic acid (C20:4 n-6) intake, which is only provided by foods of animal origin. The given data on the contribution of fish and fish product consumption to total lipid intake can be used to get a rough estimate on differences in the intake of fish oil fatty acids—n-3 PUFA eicosapentaenoic and docosahexaenoic acid—across EPIC centres. However, no conclusion on linoleic acid intake (C18:2 n-6) can be drawn because this fatty acid is provided by foods of both plant and animal origin.

In conclusion, in this large study, we describe differences and similarities in lipid intake across the EPIC cohorts of adults in 10 European countries using a recently standardized nutrient database to calculate the intake data. The heterogeneity in lipid intake shown in EPIC provides a good basis for future aetiological research on the role of different types of dietary lipids in health and disease outcomes.

Supplementary information

Supplementary information is available on the EPIC website (http://epic.iarc.fr).


Conflict of interest

M Jenab has received grant support from the World Cancer Research Fund. KT Khaw has received grant support from GB. S Bingham has received grant support from MRC Centre. The remaining authors have declared no financial interests.



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The work described in the paper 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. 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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