A standardised, national, 160-item FFQ, the FFQ-NL 1.0, was recently developed for Dutch epidemiological studies. The objective was to validate the FFQ-NL 1.0 against multiple 24-h recalls (24hR) and recovery and concentration biomarkers. The FFQ-NL 1.0 was filled out by 383 participants (25–69 years) from the Nutrition Questionnaires plus study. For each participant, one to two urinary and blood samples and one to five (mean 2·7) telephone-based 24hR were available. Group-level bias, correlation coefficients, attenuation factors, de-attenuated correlation coefficients and ranking agreement were assessed. Compared with the 24hR, the FFQ-NL 1.0 estimated the intake of energy and macronutrients well. However, it underestimated intakes of SFA and trans-fatty acids and alcohol and overestimated intakes of most vitamins by >5 %. The median correlation coefficient was 0·39 for energy and macronutrients, 0·30 for micronutrients and 0·30 for food groups. The FFQ underestimated protein intake by an average of 16 % and K by 5 %, relative to their urinary recovery biomarkers. Attenuation factors were 0·44 and 0·46 for protein and K, respectively. Correlation coefficients were 0·43–0·47 between (fatty) fish intake and plasma EPA and DHA and 0·24–0·43 between fruit and vegetable intakes and plasma carotenoids. In conclusion, the overall validity of the newly developed FFQ-NL 1.0 was acceptable to good. The FFQ-NL 1.0 is well suited for future use within Dutch cohort studies among adults.

Choline is an important nutrient for humans. Choline intake of the European population was assessed considering the European Food Safety Authority European Comprehensive Food Consumption Database and the United States Department of Agriculture Nutrient Database. Average choline intake ranges were 151–210 mg/d among toddlers (1 to ≤3 years old), 177–304 mg/d among other children (3 to ≤10 years old), 244–373 mg/d among adolescents (10 to ≤18 years old), 291–468 mg/d among adults (18 to ≤65 years old), 284–450 mg/d among elderly people (65 to ≤75 years old) and 269–444 mg/d among very elderly people (≥75 years old). The intakes were higher among males compared with females, mainly due to larger quantities of food consumed per day. In most of the population groups considered, the average choline intake was below the adequate intake (AI) set by the Institute of Medicine in the USA. The main food groups contributing to choline intake were meat, milk, grain, egg and their derived products, composite dishes and fish. The main limitations of this study are related to the absence of choline composition data of foods consumed by the European population and the subsequent assumption made to assess their intake levels. Given the definition of AI, no conclusion on the adequacy of choline intake can be drawn for most European population groups. Such results improve the knowledge on choline intake in Europe that could be further refined by the collection of choline composition data for foods as consumed in Europe.

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.

This paper aims to describe different approaches for studying the overall diet with advantages and limitations. Studies of the overall diet have emerged because the relationship between dietary intake and health is very complex with all kinds of interactions. These cannot be captured well by studying single dietary components. Three main approaches to study the overall diet can be distinguished. The first method is researcher-defined scores or indices of diet quality. These are usually based on guidelines for a healthy diet or on diets known to be healthy. The second approach, using principal component or cluster analysis, is driven by the underlying dietary data. In principal component analysis, scales are derived based on the underlying relationships between food groups, whereas in cluster analysis, subgroups of the population are created with people that cluster together based on their dietary intake. A third approach includes methods that are driven by a combination of biological pathways and the underlying dietary data. Reduced rank regression defines linear combinations of food intakes that maximally explain nutrient intakes or intermediate markers of disease. Decision tree analysis identifies subgroups of a population whose members share dietary characteristics that influence (intermediate markers of) disease. It is concluded that all approaches have advantages and limitations and essentially answer different questions. The third approach is still more in an exploration phase, but seems to have great potential with complementary value. More insight into the utility of conducting studies on the overall diet can be gained if more attention is given to methodological issues.

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.

The colours of the edible part of fruit and vegetables indicate the presence of specific micronutrients and phytochemicals. The extent to which fruit and vegetable colour groups contribute to CHD protection is unknown. We therefore examined the associations between fruit and vegetables of different colours and their subgroups and 10-year CHD incidence. We used data from a prospective population-based cohort including 20 069 men and women aged 20–65 years who were enrolled between 1993 and 1997. Participants were free of CVD at baseline and completed a validated 178-item FFQ. Hazard ratios (HR) for the association between green, orange/yellow, red/purple, white fruit and vegetables and their subgroups with CHD were calculated using multivariable Cox proportional hazards models. During 10 years of follow-up, 245 incident cases of CHD were documented. For each 25 g/d increase in the intake of the sum of all four colours of fruit and vegetables, a borderline significant association with incident CHD was found (HR 0·98; 95 % CI 0·97, 1·01). No clear associations were found for the colour groups separately. However, each 25 g/d increase in the intake of deep orange fruit and vegetables was inversely associated with CHD (HR 0·74; 95 % CI 0·55, 1·00). Carrots, their largest contributor (60 %), were associated with a 32 % lower risk of CHD (HR 0·68; 95 % CI 0·48, 0·98). In conclusion, though no clear associations were found for the four colour groups with CHD, a higher intake of deep orange fruit and vegetables and especially carrots may protect against CHD.

Anthocyanidins are bioactive flavonoids with potential health-promoting effects. These may vary among single anthocyanidins considering differences in their bioavailability and some of the mechanisms involved. The aim of the present study was to estimate the dietary intake of anthocyanidins, their food sources and the lifestyle factors (sex, age, BMI, smoking status, educational level and physisical activity) involved among twenty-seven centres in ten European countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Anthocyanidin intake and their food sources for 36 037 subjects, aged between 35 and 74 years, in twenty-seven redefined centres were obtained using standardised 24 h dietary recall software (EPIC-SOFT). An ad hoc food composition database on anthocyanidins (cyanidin, delphinidin, malvidin, pelargonidin, peonidin, petunidin) was compiled using data from the US Department of Agriculture and Phenol-Explorer databases and was expanded by adding recipes, estimated values and cooking factors. For men, the total anthocyanidin mean intake ranged from 19·83 (se 1·53) mg/d (Bilthoven, The Netherlands) to 64·88 (se 1·86) mg/d (Turin, Italy), whereas for women the range was 18·73 (se 2·80) mg/d (Granada, Spain) to 44·08 (se 2·45) mg/d (Turin, Italy). A clear south to north gradient intake was observed. Cyanidins and malvidins were the main anthocynidin contributors depending on the region and sex. Anthocyanidin intake was higher in non-obese older females, non-smokers, and increased with educational level and physical activity. The major food sources were fruits, wine, non-alcoholic beverages and some vegetables. The present study shows differences in both total and individual anthocyanidin intakes and various lifestyle factors throughout Europe, with some geographical variability in their food sources.

The use of two non-consecutive 24 h recalls using EPIC-Soft for standardised dietary monitoring in European countries has previously been proposed in the European Food Consumption Survey Method consortium. Whether this methodology is sufficiently valid to assess nutrient intake in a comparable way, among populations with different food patterns in Europe, is the subject of study in the European Food Consumption Validation consortium. The objective of the study was to compare the validity of usual protein and K intake estimated from two non-consecutive standardised 24 h recalls using EPIC-Soft between five selected centres in Europe. A total of 600 adults, aged 45–65 years, were recruited in Belgium, the Czech Republic, France, The Netherlands and Norway. From each participant, two 24 h recalls and two 24 h urines were collected. The mean and distribution of usual protein and K intake, as well as the ranking of intake, were compared with protein and K excretions within and between centres. Underestimation of protein (range 2–13 %) and K (range 4–17 %) intake was seen in all centres, except in the Czech Republic. We found a fair agreement between prevalences estimated based on the intake and excretion data at the lower end of the usual intake distribution ( < 10 % difference), but larger differences at other points. Protein and K intake was moderately correlated with excretion within the centres (ranges = 0·39–0·67 and 0·37–0·69, respectively). These were comparable across centres. In conclusion, two standardised 24 h recalls (EPIC-Soft) appear to be sufficiently valid for assessing and comparing the mean and distribution of protein and K intake across five centres in Europe as well as for ranking individuals.

Salt is the main vehicle for iodine fortification in The Netherlands. A reduction in salt intake may reduce the supply of iodine. Our aim was to quantify the effect of salt reduction on the habitual iodine intake of the Dutch population and the risk of inadequate iodine intake. We used data of the Dutch National Food Consumption Survey (1997–8) and an update of the food composition database to estimate habitual salt and iodine intake. To take into account uncertainty about the use of iodised salt (industrial and discretionary) and food supplements, a simulation model was used. Habitual iodine and salt intakes were simulated for scenarios of salt reduction and compared with no salt reduction. With 12, 25 and 50 % salt reduction in industrially processed foods, the iodine intake remained adequate for a large part of the Dutch population. For the extreme scenario of a 50 % reduction in both industrially and discretionary added salt, iodine intake might become inadequate for part of the Dutch population (up to 10 %). An increment of the proportion of industrially processed foods using iodised salt or a small increase in iodine salt content will solve this. Nevertheless, 8–35 % of 1- to 3-year-old children might have iodine intakes below the corresponding estimated average requirement (EAR), depending on the salt intake scenario. This points out the need to review the EAR value for this age group or to suggest the addition of iodine to industrially manufactured complementary foods.

The authors automated the selection of foods in a computer system that compiles and processes tailored FFQ. For the selection of food items, several methods are available. The aim of the present study was to compare food lists made by MOM2, which identifies food items with highest between-person variance in intake of the nutrients of interest without taking other items into account, with food lists made by forward regression. The name MOM2 refers to the variance, which is the second moment of the nutrient intake distribution. Food items were selected for the nutrients of interest from 2 d of recorded intake in 3524 adults aged 25–65 years. Food lists by 80 % MOM2 were compared to those by 80 % explained variance for regression on differences between the number and type of food items, and were evaluated on (1) the percentage of explained variance and (2) percentage contribution to population intake computed for the selected items on the food list. MOM2 selected the same food items for Ca, a few more for fat and vitamin C, and a few less for carbohydrates and dietary fibre than forward regression. Food lists by MOM2 based on 80 % of variance in intake covered 75–87 % of explained variance for different nutrients by regression and contributed 53–75 % to total population intake. Concluding, for developing food lists of FFQ, it appears sufficient to select food items based on the contribution to variance in nutrient intake without taking covariance into account.