Eating out has been linked to the current obesity epidemic, but the evaluation of the extent to which out of home (OH) dietary intakes are different from those at home (AH) is limited. Data collected among 8849 men and 14 277 women aged 35–64 years from the general population of eleven European countries through 24-h dietary recalls or food diaries were analysed to: (1) compare food consumption OH to those AH; (2) describe the characteristics of substantial OH eaters, defined as those who consumed 25 % or more of their total daily energy intake at OH locations. Logistic regression models were fit to identify personal characteristics associated with eating out. In both sexes, beverages, sugar, desserts, sweet and savoury bakery products were consumed more OH than AH. In some countries, men reported higher intakes of fish OH than AH. Overall, substantial OH eating was more common among men, the younger and the more educated participants, but was weakly associated with total energy intake. The substantial OH eaters reported similar dietary intakes OH and AH. Individuals who were not identified as substantial OH eaters reported consuming proportionally higher quantities of sweet and savoury bakery products, soft drinks, juices and other non-alcoholic beverages OH than AH. The OH intakes were different from the AH ones, only among individuals who reported a relatively small contribution of OH eating to their daily intakes and this may partly explain the inconsistent findings relating eating out to the current obesity epidemic.

Eating out is often recorded through short-term measurements and the large within-person variability in intakes may not be adequately captured. The present study aimed to understand the effect of measurement error when using eating-out data from one or two 24 h dietary recalls (24hDR), in order to describe intakes and assess associations between eating out and personal characteristics. In a sample of 366 adults from Potsdam, Germany, two 24hDR and a FFQ were collected. Out-of-home intakes were estimated based on either one 24hDR or two 24hDR or the Multiple Source Method (MSM) combining the two 24hDR and the questionnaire. The distribution of out-of-home intakes of energy, macronutrients and selected foods was described. Multiple linear regression and partial correlation coefficients were estimated to assess associations between out-of-home energy intake and participants' characteristics. The mean daily out-of-home intakes estimated from the two 24hDR were similar to the usual intakes estimated through the MSM. The out-of-home energy intake, estimated through either one or two 24hDR, was positively associated with total energy intake, inversely with age and associations were stronger when using the two 24hDR. A marginally significant inverse association between out-of-home energy intake and physical activity at work was observed only on the basis of the two 24hDR. After applying the MSM, all significant associations remained and were more precise. Data on eating out collected through one or two 24hDR may not adequately describe intake distributions, but significant associations between eating out and participants' characteristics are highly unlikely to appear when in reality these do not exist.

Fish consumption is the major dietary source of EPA and DHA, which according to rodent experiments may reduce body fat mass and prevent obesity. Only a few human studies have investigated the association between fish consumption and body-weight gain. We investigated the association between fish consumption and subsequent change in body weight. Women and men (n 344 757) participating in the European Prospective Investigation into Cancer and Nutrition were followed for a median of 5·0 years. Linear and logistic regression were used to investigate the associations between fish consumption and subsequent change in body weight. Among women, the annual weight change was 5·70 (95 % CI 4·35, 7·06), 2·23 (95 % CI 0·16, 4·31) and 11·12 (95 % CI 8·17, 14·08) g/10 g higher total, lean and fatty fish consumption per d, respectively. The OR of becoming overweight in 5 years among women who were normal weight at enrolment was 1·02 (95 % CI 1·01, 1·02), 1·01 (95 % CI 1·00, 1·02) and 1·02 (95 % CI 1·01, 1·04) g/10 g higher total, lean and fatty consumption per d, respectively. Among men, fish consumption was not statistically significantly associated with weight change. Adjustment for potential over- or underestimation of fish consumption did not systematically change the observed associations, but the 95 % CI became wider. The results in subgroups from analyses stratified by age or BMI at enrolment were not systematically different. In conclusion, the present study suggests that fish consumption has no appreciable association with body-weight gain.

Folate plays an important role in the synthesis and methylation of DNA as a cofactor in one-carbon metabolism. Inadequate folate intake has been linked to adverse health events. However, comparable information on dietary folate intake across European countries has never been reported. The objective of the present study was to describe the dietary folate intake and its food sources in ten countries in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. A cross-sectional analysis was conducted in 36 034 participants (aged 35–74 years) who completed a single 24 h dietary recall using a computerised interview software program, EPIC-Soft® (International Agency for Research on Cancer, Lyon). Dietary folate intake was estimated using the standardised EPIC Nutrient DataBase, adjusted for age, energy intake, weight and height and weighted by season and day of recall. Adjusted mean dietary folate intake in most centres ranged from 250 to 350 μg/d in men and 200 to 300 μg/d in women. Folate intake tended to be lower among current smokers and heavier alcohol drinkers and to increase with educational level, especially in women. Supplement users (any types) were likely to report higher dietary folate intake in most centres. Vegetables, cereals and fruits, nuts and seeds were the main contributors to folate intake. Nonetheless, the type and pattern of consumption of these main food items varied across the centres. These first comparisons of standardised dietary folate intakes across different European populations show moderate regional differences (except the UK health conscious group), and variation by sex, educational level, smoking and alcohol-drinking status, and supplement use.

Whether there are differences between countries in the validity of self-reported diet in relation to BMI, as evaluated using recovery biomarkers, is not well understood. We aimed to evaluate BMI-related reporting errors on 24 h dietary recalls (24-HDR) and on dietary questionnaires (DQ) using biomarkers for protein and K intake and whether the BMI effect differs between six European countries. Between 1995 and 1999, 1086 men and women participating in the European Prospective Investigation into Cancer and Nutrition completed a single 24-HDR, a DQ and one 24 h urine collection. In regression analysis, controlling for age, sex, education and country, each unit (1 kg/m2) increase in BMI predicted an approximately 1·7 and 1·3 % increase in protein under-reporting on 24-HDR and DQ, respectively (both P < 0·0001). Exclusion of individuals who probably misreported energy intake attenuated BMI-related bias on both instruments. The BMI effect on protein under-reporting did not differ for men and women and neither between countries on both instruments as tested by interaction (all P>0·15). In women, but not in men, the DQ yielded higher mean intakes of protein that were closer to the biomarker-based measurements across BMI groups when compared with 24-HDR. Results for K were similar to those of protein, although BMI-related under-reporting of K was of a smaller magnitude, suggesting differential misreporting of foods. Under-reporting of protein and K appears to be predicted by BMI, but this effect may be driven by ‘low-energy reporters’. The BMI effect on under-reporting seems to be the same across countries.

Overall dietary patterns have been associated with health and longevity. We used principal component (PC) and cluster analyses to identify the prevailing dietary patterns of 99 744 participants, aged 60 years or older, living in nine European countries and participating in the European Prospective Investigation into Cancer and Nutrition (EPIC-Elderly cohort) and to examine their socio-demographic and lifestyle correlates. Two PC were identified: PC1 reflects a ‘vegetable-based’ diet with an emphasis on foods of plant origin, rice, pasta and other grain rather than on margarine, potatoes and non-alcoholic beverages. PC2 indicates a ‘sweet- and fat-dominated’ diet with a preference for sweets, added fat and dairy products but not meat, alcohol, bread and eggs. PC1 was associated with a younger age, a higher level of education, physical activity, a higher BMI, a lower waist:hip ratio and never and past smoking. PC2 was associated with older age, less education, never having smoked, a lower BMI and waist:hip ratio and lower levels of physical activity. Elderly individuals in southern Europe scored positively on PC1 and about zero on PC2, whereas the elderly in northern Europe scored negatively on PC1 and variably on PC2. The results of cluster analysis were compatible with the indicated dietary patterns. ‘Vegetable-based’ and a ‘sweet- and fat-dominated’ diets are prevalent among the elderly across Europe, and there is a north–south gradient regarding their dietary choices. Our study contributes to the identification of groups of elderly who are likely to have different prospects for long-term disease occurrence and survival.

Dietary patterns are comprehensive variables of dietary intake appropriate to model the complex exposure in nutritional research. The objectives of this study were to identify dietary patterns by applying two statistical methods, principal component analysis (PCA) and reduced rank regression (RRR), and to assess their ability to predict all-cause mortality. Motivated by previous studies we chose percentages of energy from different macronutrients as response variables in the RRR analysis. We used data from 9356 German elderly subject enrolled in the European Prospective Investigation into Cancer and Nutrition study. The first RRR pattern, subjects which explained 30·8 % of variation in energy sources and especially much variation in intake of saturated fat, monounsaturated fat and carbohydrates was a significant predictor of all-cause mortality. The pattern score had high positive loadings in all types of meat, butter, sauces and eggs, and was inversely associated with bread and fruits. After adjustment for other known risk factors, the relative risks from the lowest to highest quintiles of the first RRR pattern score were 1·0, 1·01, 0·96, 1·32, 1·61 (P for trend: 0·0004). In contrast, the first two PCA patterns explaining 19·7 % of food intake variation but only 7·0 % of variation in energy sources were not related to mortality. These results suggest that variation in macronutrients is meaningful for mortality and that the RRR method is more appropriate than the classic PCA method to identify dietary patterns relevant to mortality.