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.

Generally, there is a need for short questionnaires to estimate diet quality in the Netherlands. We developed a thirty-four-item FFQ – the Dutch Healthy Diet FFQ (DHD-FFQ) – to estimate adherence to the most recent Dutch guidelines for a healthy diet of 2006 using the DHD-index. The objectives of the present study were to evaluate the DHD-index derived from the DHD-FFQ by comparing it with the index based on a reference method and to examine associations with participant characteristics, nutrient intakes and levels of cardiometabolic risk factors. Data of 1235 Dutch men and women, aged between 20 and 70 years, participating in the Nutrition Questionnaires plus study were used. The DHD-index was calculated from the DHD-FFQ and from a reference method consisting of a 180-item FFQ combined with a 24-h urinary Na excretion value. Ranking was studied using Spearman’s correlations, and absolute agreement was studied using a Bland–Altman plot. Nutrient intakes derived from the 180-item FFQ were studied according to quintiles of the DHD-index using DHD-FFQ data. The correlation between the DHD-index derived from the DHD-FFQ and the reference method was 0·56 (95 % CI 0·52, 0·60). The Bland–Altman plot showed a small mean overestimation of the DHD-index derived from the DHD-FFQ compared with the reference method. The DHD-index score was in the favourable direction associated with most macronutrient and micronutrient intakes when adjusted for energy intake. No associations between the DHD-index score and cardiometabolic risk factors were observed. In conclusion, the DHD-index derived from the DHD-FFQ was considered acceptable in ranking but relatively poor in individual assessment of diet quality.

Information on dietary composition is vitally important for elite athletes to optimise their performance and recovery, which requires valid tools. The aim of the present study was to investigate the validity of assessing protein intake using three web-based 24-h recalls and questionnaires, by comparing these with three urinary N excretions on the same day. A total of forty-seven Dutch elite top athletes, both disabled and non-disabled, aged between 18 and 35 years, with a BMI of 17·5–31 kg/m2, exercising >12 h/week were recruited. Estimated mean dietary protein intake was 109·6 (sd 33·0) g/d by recalls and questionnaires v. 141·3 (sd 38·2) g/d based on N excretions in urine; the difference was 25·5 (sd 21·3) % between the methods (P<0·05). We found a reasonably good association between methods for protein intake of 0·65 (95 % CI 0·45, 0·79). On an individual level, under-reporting was larger with higher protein intakes than with lower intakes. No significant differences were found in reporting absolute differences between subcategories (sex, under-reporting, BMI, collection of recalls within a certain amount of time and using protein supplements or not). In conclusion, combined, multiple, 24-h recalls and questionnaires underestimated protein intake in these young elite athletes more than that reported for non-athlete populations. The method proved to be suitable for ranking athletes according to their protein intake as needed in epidemiological studies. On an individual level, the magnitude of underestimation was about equal for all athletes except for those with very high protein intakes.

As FFQ are subject to measurement error, associations between self-reported intake by FFQ and outcome measures should be adjusted by correction factors obtained from a validation study. Whether the correction is adequate depends on the characteristics of the reference method used in the validation study. Preferably, reference methods should (1) be unbiased and (2) have uncorrelated errors with those in the FFQ. The aim of the present study was to assess the validity of the duplicate portion (DP) technique as a reference method and compare its validity with that of a commonly used reference method, the 24 h recall (24hR), for protein, K and Na using urinary markers as the unbiased reference method. For 198 subjects, two DP, two FFQ, two urinary biomarkers and between one and fifteen 24hR (web based and/or telephone based) were collected within 1·5 years. Multivariate measurement error models were used to estimate bias, error correlations between FFQ and DP or 24hR, and attenuation factors of these methods. The DP was less influenced by proportional scaling bias (0·58 for protein, 0·72 for K and 0·52 for Na), and correlated errors between DP and FFQ were lowest (protein 0·28, K 0·17 and Na 0·19) compared with the 24hR. Attenuation factors (protein 0·74, K 0·54 and Na 0·43) also indicated that the DP performed better than the 24hR. Therefore, the DP is probably the best available reference method for FFQ validation for nutrients that currently have no generally accepted recovery biomarker.

Studies using 24 h urine collections need to incorporate ways to validate the completeness of the urine samples. Models to predict urinary creatinine excretion (UCE) have been developed for this purpose; however, information on their usefulness to identify incomplete urine collections is limited. We aimed to develop a model for predicting UCE and to assess the performance of a creatinine index using para-aminobenzoic acid (PABA) as a reference. Data were taken from the European Food Consumption Validation study comprising two non-consecutive 24 h urine collections from 600 subjects in five European countries. Data from one collection were used to build a multiple linear regression model to predict UCE, and data from the other collection were used for performance testing of a creatinine index-based strategy to identify incomplete collections. Multiple linear regression (n 458) of UCE showed a significant positive association for body weight (β = 0·07), the interaction term sex × weight (β = 0·09, reference women) and protein intake (β = 0·02). A significant negative association was found for age (β = − 0·09) and sex (β = − 3·14, reference women). An index of observed-to-predicted creatinine resulted in a sensitivity to identify incomplete collections of 0·06 (95 % CI 0·01, 0·20) and 0·11 (95 % CI 0·03, 0·22) in men and women, respectively. Specificity was 0·97 (95 % CI 0·97, 0·98) in men and 0·98 (95 % CI 0·98, 0·99) in women. The present study shows that UCE can be predicted from weight, age and sex. However, the results revealed that a creatinine index based on these predictions is not sufficiently sensitive to exclude incomplete 24 h urine collections.