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.
The World Health Organization (WHO) has formulated guidelines for a healthy diet to prevent chronic diseases and postpone death worldwide. Our objective was to investigate the association between the ...WHO guidelines, measured using the Healthy Diet Indicator (HDI), and all-cause mortality in elderly men and women from Europe and the United States. We analyzed data from 396,391 participants (42% women) in 11 prospective cohort studies who were 60 years of age or older at enrollment (in 1988-2005). HDI scores were based on 6 nutrients and 1 food group and ranged from 0 (least healthy diet) to 70 (healthiest diet). Adjusted cohort-specific hazard ratios were derived by using Cox proportional hazards regression and subsequently pooled using random-effects meta-analysis. During 4,497,957 person-years of follow-up, 84,978 deaths occurred. Median HDI scores ranged from 40 to 54 points across cohorts. For a 10-point increase in HDI score (representing adherence to an additional WHO guideline), the pooled adjusted hazard ratios were 0.90 (95% confidence interval (CI): 0.87, 0.93) for men and women combined, 0.89 (95% CI: 0.85, 0.92) for men, and 0.90 (95% CI: 0.85, 0.95) for women. These estimates translate to an increased life expectancy of 2 years at the age of 60 years. Greater adherence to the WHO guidelines is associated with greater longevity in elderly men and women in Europe and the United States.
To illustrate the impact of combining 24 h recall (24hR) and FFQ estimates using regression calibration (RC) and enhanced regression calibration (ERC) on diet-disease associations.
Wageningen area, ...the Netherlands, 2011-2013.
Five approaches for obtaining self-reported dietary intake estimates of protein and K were compared: (i) uncorrected FFQ intakes (FFQ); (ii) uncorrected average of two 24hR ( $\overline {\rm R}$ ); (iii) average of FFQ and $\overline {\rm R}$ ( ${\overline {\rm F}}\,\overline {\rm R}}$ ); (iv) RC from regression of 24hR v. FFQ; and (v) ERC by adding individual random effects to the RC approach. Empirical attenuation factors (AF) were derived by regression of urinary biomarker measurements v. the resulting intake estimates.
Data of 236 individuals collected within the National Dietary Assessment Reference Database.
Both FFQ and 24hR dietary intake estimates were measured with substantial error. Using statistical techniques to correct for measurement error (i.e. RC and ERC) reduced bias in diet-disease associations as indicated by their AF approaching 1 (RC 1·14, ERC 0·95 for protein; RC 1·28, ERC 1·34 for K). The larger sd and narrower 95% CI of AF obtained with ERC compared with RC indicated that using ERC has more power than using RC. However, the difference in AF between RC and ERC was not statistically significant, indicating no significantly better de-attenuation by using ERC compared with RC. AF larger than 1, observed for the ERC for K, indicated possible overcorrection.
Our study highlights the potential of combining FFQ and 24hR data. Using RC and ERC resulted in less biased associations for protein and K.
Abstract
Objective
To provide the evidence base for targeted nutrition policies to reduce the risk of micronutrient/diet-related diseases among disadvantaged populations in Europe, by focusing on: ...folate, vitamin B
12
, Fe, Zn and iodine for intake and status; and vitamin C, vitamin D, Ca, Se and Cu for intake.
Design
MEDLINE and Embase databases were searched to collect original studies that: (i) were published from 1990 to 2011; (ii) involved >100 subjects; (iii) had assessed dietary intake at the individual level; and/or (iv) included best practice biomarkers reflecting micronutrient status. We estimated relative differences in mean micronutrient intake and/or status between the lowest and highest socio-economic groups to: (i) evaluate variation in intake and status between socio-economic groups; and (ii) report on data availability.
Setting
Europe.
Subjects
Children, adults and elderly.
Results
Data from eighteen publications originating primarily from Western Europe showed that there is a positive association between indicators of socio-economic status and micronutrient intake and/or status. The largest differences were observed for intake of vitamin C in eleven out of twelve studies (5–47 %) and for vitamin D in total of four studies (4–31 %).
Conclusions
The positive association observed between micronutrient intake and socio-economic status should complement existing evidence on socio-economic inequalities in diet-related diseases among disadvantaged populations in Europe. These findings could provide clues for further research and have implications for public health policy aimed at improving the intake of micronutrients and diet-related diseases.
Observational studies performed in Asian populations suggest that eating rate is related to BMI. This paper investigates the association between self-reported eating rate (SRER) and body mass index ...(BMI) in a Dutch population, after having validated SRER against actual eating rate.
Two studies were performed; a validation and a cross-sectional study. In the validation study SRER (i.e., 'slow', 'average', or 'fast') was obtained from 57 participants (men/women = 16/41, age: mean ± SD = 22.6 ± 2.8 yrs., BMI: mean ± SD = 22.1 ± 2.8 kg/m
) and in these participants actual eating rate was measured for three food products. Using analysis of variance the association between SRER and actual eating rate was studied. The association between SRER and BMI was investigated in cross-sectional data from the NQplus cohort (i.e., 1473 Dutch adults; men/women = 741/732, age: mean ± SD = 54.6 ± 11.7 yrs., BMI: mean ± SD = 25.9 ± 4.0 kg/m
) using (multiple) linear regression analysis.
In the validation study actual eating rate increased proportionally with SRER (for all three food products P < 0.01). In the cross-sectional study SRER was positively associated with BMI in both men and women (P = 0.03 and P < 0.001, respectively). Self-reported fast-eating women had a 1.13 kg/m
(95% CI 0.43, 1.84) higher BMI compared to average-speed-eating women, after adjusting for confounders. This was not the case in men; self-reported fast-eating men had a 0.29 kg/m
(95% CI -0.22, 0.80) higher BMI compared to average-speed-eating men, after adjusting for confounders.
These studies show that self-reported eating rate reflects actual eating rate on a group-level, and that a high self-reported eating rate is associated with a higher BMI in this Dutch population.
Cardiovascular disease (CVD) represents a leading cause of mortality worldwide, especially in the elderly. Lowering the number of CVD deaths requires preventive strategies targeted on the elderly.
...The objective was to generate evidence on the association between WHO dietary recommendations and mortality from CVD, coronary artery disease (CAD), and stroke in the elderly aged ≥60 y.
We analyzed data from 10 prospective cohort studies from Europe and the United States comprising a total sample of 281,874 men and women free from chronic diseases at baseline. Components of the Healthy Diet Indicator (HDI) included saturated fatty acids, polyunsaturated fatty acids, mono- and disaccharides, protein, cholesterol, dietary fiber, and fruit and vegetables. Cohort-specific HRs adjusted for sex, education, smoking, physical activity, and energy and alcohol intakes were pooled by using a random-effects model.
During 3,322,768 person-years of follow-up, 12,492 people died of CVD. An increase of 10 HDI points (complete adherence to an additional WHO guideline) was, on average, not associated with CVD mortality (HR: 0.94; 95% CI: 0.86, 1.03), CAD mortality (HR: 0.99; 95% CI: 0.85, 1.14), or stroke mortality (HR: 0.95; 95% CI: 0.88, 1.03). However, after stratification of the data by geographic region, adherence to the HDI was associated with reduced CVD mortality in the southern European cohorts (HR: 0.87; 95% CI: 0.79, 0.96; I(2) = 0%) and in the US cohort (HR: 0.85; 95% CI: 0.83, 0.87; I(2) = not applicable).
Overall, greater adherence to the WHO dietary guidelines was not significantly associated with CVD mortality, but the results varied across regions. Clear inverse associations were observed in elderly populations in southern Europe and the United States.
It is unclear to what extent adherence to dietary guidelines may specifically affect visceral fat and liver fat. We aimed to study the association between the Dutch Healthy Diet Index (DHD-index) and ...total body fat, visceral adipose tissue (VAT) and hepatic triglyceride content (HTGC) in middle-aged men and women.
In this cross-sectional study, VAT was assessed by magnetic resonance imaging (MRI) in 2580 participants, and HTGC by proton-MR spectroscopy in 2083 participants. Habitual dietary intake and physical activity were estimated by questionnaire. Adherence to the current Dutch dietary guidelines was estimated by the 2015 DHD-index score based on the thirteen components (vegetables, fruit, wholegrain products, legumes, nuts, dairy, fish, tea, liquid fats, red meat, processed meat, sweetened beverages, and alcohol). The DHD-index ranges between 0 and 130 with a higher score indicating a healthier diet. We used linear regression to examine associations of the DHD-index with VAT and HTGC, adjusted for age, smoking, education, ethnicity, basal metabolic rate, energy restricted diet, menopausal state, physical activity, total energy intake, and total body fat. We additionally excluded the components one by one to examine individual contributions to the associations.
Included participants (43% men) had a mean (SD) age of 56 (6) years and DHD-index score of 71 (15). A 10-point higher DHD-index score was associated with 2.3 cm
less visceral fat (95% CI; -3.5; -1.0 cm
) and less liver fat (0.94 times, 95% CI; 0.90; 0.98). Of all components, exclusion of dairy attenuated the associations with TBF and VAT.
Adherence to the dietary guidelines as estimated by the DHD-index was associated with less total body fat, and with less visceral and liver fat after adjustment for total body fat. These findings might contribute to better understanding of the mechanisms underlying associations between dietary habits and cardiometabolic diseases.
The need for a better understanding of food consumption behaviour within its behavioural context has sparked the interest of nutrition researchers for user-documented food consumption data collected ...outside the research context using publicly available nutrition apps. The study aims to characterize the scientific, technical, legal and ethical features of this data in order to identify the opportunities and challenges associated with using this data for nutrition research.
A search for apps collecting food consumption data was conducted in October 2016 against UK Google Play and iTunes storefronts. 176 apps were selected based on user ratings and English language support. Publicly available information from the app stores and app-related websites was investigated and relevant data extracted and summarized. Our focus was on characteristics related to scientific relevance, data management and legal and ethical governance of user-documented food consumption data.
Food diaries are the most common form of data collection, allowing for multiple inputs including generic food items, packaged products, or images. Standards and procedures for compiling food databases used for estimating energy and nutrient intakes remain largely undisclosed. Food consumption data is interlinked with various types of contextual data related to behavioural motivation, physical activity, health, and fitness. While exchange of data between apps is common practise, the majority of apps lack technical documentation regarding data export. There is a similar lack of documentation regarding the implemented terms of use and privacy policies. While users are usually the owners of their data, vendors are granted irrevocable and royalty free licenses to commercially exploit the data.
Due to its magnitude, diversity, and interconnectedness, user-documented food consumption data offers promising opportunities for a better understanding of habitual food consumption behaviour and its determinants. Non-standardized or non-documented food data compilation procedures, data exchange protocols and formats, terms of use and privacy statements, however, limit possibilities to integrate, process and share user-documented food consumption data. An ongoing research effort is required, to keep pace with the technical advancements of food consumption apps, their evolving data networks and the legal and ethical regulations related to protecting app users and their personal data.
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