Development and external validation of the 'Flower-FFQ': a FFQ designed for the Lifelines Cohort Study.

OBJECTIVE: FFQ assess habitual dietary intake and are relatively inexpensive to process, but may take up to 60 min to complete. This article describes the validation of the Flower-FFQ, which consists of four short FFQ measuring the intake of energy and macronutrients or specific (micro)nutrients/foods that can be merged into one complete daily assessment using predefined algorithms. DESIGN: Participants completed the Flower-FFQ and validated regular-FFQ (n 401). Urinary N (n 242) and K excretions (n 361) were measured. We evaluated: (1) group-level bias, (2) correlations and (3) cross-classification. SETTING: Observational study. PARTICIPANTS: Dutch adults, 54 ± 11 (mean ± SD) years. RESULTS: Flower-FFQ1, Flower-FFQ2, Flower-FFQ3 and Flower-FFQ4 were completed in ±24, 9, 8 and 9 min (±50 min total), respectively. The regular-FFQ was completed in ±43 min. Mean energy (flower v. regular: 7953 v. 8718 kJ/d) and macronutrient intakes (carbohydrates: 204 v. 222 g/d; protein: 75 v. 76 g/d; fat: 74 v. 83 g/d; ethanol: 8 v. 12 g/d) were comparatively similar. Spearman correlations between Flower-FFQ and regular-FFQ ranged from 0·60 to 0·80 for macronutrients and from 0·40 to 0·80 for micronutrients and foods. For all micronutrients and foods, ≥ 78 % of the participants classified in the same/adjacent quartile. The Flower-FFQ underestimated urinary N and K excretions by 24 and 18 %; 75 and 73 % of the participants ranked in the same/adjacent quartile. CONCLUSION: Completing the Flower-FFQ required 50 min with a maximum of 25 min per short FFQ. The Flower-FFQ has a moderate to good ranking ability for most nutrients and foods and performs sufficiently to study diet-disease associations.

The accuracy of portion size estimation using food images and textual descriptions of portion sizes: an evaluation study.

BACKGROUND: Inaccurate self-report of portion sizes is a major cause of measurement error in dietary assessment. To reduce this error, different portion size estimation aids (PSEAs) have been developed, including food images (image based, IB-PSE) and textual descriptions of portion sizes (text-based, TB-PSE). We assessed the accuracy of portion size estimation by IB-PSE and TB-PSE. METHODS: True intake of one lunch was ascertained in forty participants. Self-reported portion sizes were assessed after 2 and 24 hours by means of TB-PSE and IB-PSE, in random order. Wilcoxon's tests were used to compare mean true intakes to reported intakes. Moreover, proportions of reported portion sizes within 10% and 25% of true intake were assessed. An adapted Bland-Altman approach was used to assess agreement between true and reported portion sizes. Analyses were conducted for all foods and drinks combined and for predetermined food types. RESULTS: No significant differences were observed between reported portion sizes at 2 and 24 hours after lunch. Combining median relative errors of all foods items resulted in an overall 0% error rate for TB-PSE and 6% error rate for IB-PSE. Comparing reported portion sizes within 10% (31% vs. 13%) and 25% (50% vs. 35%) of the true intake showed a better performance for TB-PSE compared to IP-PSE, respectively. Bland-Altman plots indicated a higher agreement between reported and true intake for TB-PSE compared to IB-PSE. CONCLUSIONS: Although the use of TB-PSE still results in measurement error, our results suggest a more accurate dietary intake assessment with TB-PSE than IB-PSE.

Adherence to dietary guidelines in relation to visceral fat and liver fat in middle-aged men and women: the NEO study.

BACKGROUND: 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. DESIGN: 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. RESULTS: 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 cm2 less visceral fat (95% CI; -3.5; -1.0 cm2) 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. CONCLUSIONS: 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.

Adherence to a food group-based dietary guideline and incidence of prediabetes and type 2 diabetes.

PURPOSE: In this study, we investigated the association between adherence to the Dutch Healthy Diet index 2015 (DHD15-index) and incidence of prediabetes (preT2D) and Type 2 Diabetes (T2D) in a representative sample for the general Dutch population. METHODS: Two prospective cohort studies, The Hoorn and The New Hoorn Study, were used for data analyses. In total, data from 2951 participants without diabetes at baseline (mean age 56.5 ± 7.5 years; 49.6% male) were harmonized. Baseline dietary intake was assessed with validated Food Frequency Questionnaires and adherence to the DHD15-index was calculated (range 0-130). PreT2D and T2D were classified according to the WHO criteria 2011. Poisson regression was used to estimate prevalence ratios between participant scores on the DHD15-index and preT2D and T2D, adjusted for follow-up duration, energy intake, socio-demographic, and lifestyle factors. Change in fasting plasma glucose levels (mmol/L) over follow-up was analysed using linear regression analyses, additionally adjusted for baseline value. RESULTS: During a mean follow-up of 6.3 ± 0.7 years, 837 participants developed preT2D and 321 participants developed T2D. The highest adherence to the DHD15-index was significantly associated with lower T2D incidence [model 3, PRT3vsT1: 0.70 (0.53; 0.92), ptrend = 0.01]. The highest adherence to the DHD15-index pointed towards a lower incidence of preT2D [PRT3vsT1: 0.87 (0.74; 1.03), ptrend = 0.11]. Higher adherence to the DHD15-index was not associated with change in fasting plasma glucose levels [ß10point: - 0.012 (- 0.034; 0.009)mmol/L]. CONCLUSION: The present study showed that the highest compared to the lowest adherence to the DHD15-index was associated with a lower T2D incidence, and pointed towards a lower incidence of preT2D. These results support the benefits of adhering to the guidelines in T2D prevention.

Sweet Snacks Are Positively and Fruits and Vegetables Are Negatively Associated with Visceral or Liver Fat Content in Middle-Aged Men and Women.

BACKGROUND: Visceral adipose tissue (VAT) and hepatic triglyceride content (HTGC) are major risk factors for cardiometabolic diseases. OBJECTIVE: We aimed to investigate the association of dietary intake of the main food groups with VAT and HTGC in middle-aged men and women. METHODS: We used data from the Netherlands Epidemiology of Obesity study, a population-based study including 6671 participants aged 45-65 y at baseline. In this cross-sectional analysis, VAT and HTGC were assessed by magnetic resonance imaging and spectroscopy, respectively, as the primary outcomes. Habitual intake of main food groups (dairy, meat, fish, fruits and vegetables, sweet snacks, and fats and oils) was estimated through the use of a food-frequency questionnaire. We examined associations of intake of different food groups with VAT and HTGC by linear regression analysis stratified by sex and adjusted for age, smoking, education, ethnicity, physical activity, basal metabolic rate, energy-restricted diet, menopausal state, and total energy intake. RESULTS: In women, a 100-g/d higher intake of dairy was associated with 2.0 cm2 less VAT (95% CI: -3.4, -0.7 cm2) and a 0.95-fold lower HTGC (95% CI: 0.90-, 0.99-fold). Moreover, a 100-g/d higher intake of fruit and vegetables was associated with 1.6 cm2 less VAT (95% CI: -2.9, -0.2 cm2) in women. Fruit and vegetables were negatively associated (0.95; 95% CI: 0.91, 1.00) with HTGC, and sweet snacks were positively associated (1.29; 95% CI: 1.03, 1.63). Patterns were weaker but similar in men. Fish intake was not associated with VAT or HTGC and plant-based fat and oil intake were only associated with VAT after adjustment for total body fat. CONCLUSIONS: Despite some variation in the strength of the associations between men and women, dietary intake of sweet snacks was positively associated with HTGC, and fruit and vegetable intake were negatively associated with visceral and liver fat content. Prospective studies are needed to confirm these results. The Netherlands Epidemiology of Obesity study is registered at clinicaltrials.gov with identifier NCT03410316.

Consumption of Alcoholic and Sugar-Sweetened Beverages is Associated with Increased Liver Fat Content in Middle-Aged Men and Women.

BACKGROUND: Fatty liver is the leading cause of chronic liver diseases and increases the risk of cardiovascular disease. Besides alcohol consumption, energy-containing nonalcoholic beverages may contribute to liver fat accumulation. OBJECTIVE: We aimed to study the consumption of alcoholic and nonalcoholic beverages and their mutual replacement in relation to hepatic triglyceride content (HTGC) in middle-aged men and women. METHODS: In this cross-sectional analysis, HTGC was assessed by proton magnetic resonance spectroscopy. Habitual consumption of alcoholic and nonalcoholic beverages was assessed using a validated food-frequency questionnaire. All beverages were converted to standard servings and to percentage of total energy intake (En%). We performed linear regression to examine the association of alcoholic and nonalcoholic beverages with HTGC, adjusted for age, sex, smoking, education, ethnicity, physical activity, total energy intake, and total body fat. We studied replacement of alcoholic beverages with nonalcoholic beverages per 1 serving/d and per 5 En%/d. RESULTS: After exclusion of individuals with missing values, 1966 participants (47% men) were analyzed, with a mean ± SD age of 55 ± 6 y, BMI of 26 ± 4 kg/m2, and HTGC of 5.7% ± 7.9%. Each extra alcoholic serving per day was associated with more liver fat (1.09 times; 95% CI: 1.05, 1.12). Replacing 5 En% of alcoholic beverages with milk was associated with less liver fat (0.89 times; 95% CI: 0.81, 0.98), whereas replacement with 5 En% of sugar-sweetened beverages was associated with liver fat to an extent similar to alcoholic beverages (1.00 times; 95% CI: 0.91, 1.09). CONCLUSION: In a population-based cohort, consumption of each extra daily alcoholic beverage was associated with more liver fat. In isocaloric replacement of alcoholic beverages, milk was associated with less liver fat, whereas sugar-sweetened beverages were equally associated with liver fat. This suggests that intake of alcohol and sugars may contribute to liver fat accumulation. This trial was registered at clinicaltrials.gov as NCT03410316.

Diet quality in childhood: the Generation R Study.

PURPOSE: We aimed to evaluate diet quality of 8-year-old children in the Netherlands, to identify sociodemographic and lifestyle correlates of child diet quality, and to examine tracking of diet quality from early to mid-childhood. METHODS: For 4733 children participating in a population-based cohort, we assessed dietary intake using a validated food-frequency questionnaire at a median age of 8.1 years (interquartile range 8.0-8.2) (2011-2014). Based on dietary guidelines, we developed and validated a food-based diet quality score for children consisting of ten components (score 0-10): sufficient intake of vegetables; fruit; whole grains; fish; legumes; nuts; dairy; oils and soft fats; and low intake of sugar-containing-beverages; and high-fat and processed meat. RESULTS: We observed a mean (± SD) diet quality score of 4.5 (± 1.2) out of a maximum of 10. On average, intake of legumes, nuts, and oils or soft fats was below recommendations, whereas intake of sugar-containing beverages and high-fat or processed meat was higher than recommended. The main factors associated with higher diet quality were higher maternal educational level (ß = 0.29, 95% CI 0.21, 0.37 versus low education), higher household income (ß = 0.15, 95% CI 0.05, 0.25 versus low income), no maternal smoking (ß = 0.13, 95% CI 0.02, 0.25 versus current smoking), and less screen time (ß = 0.31, 95% CI 0.24, 0.38)-all independent of each other. For children with available dietary data at age 1 year (n = 2608), we observed only weak tracking of diet quality from early to mid-childhood (Pearson's r = 0.19, k = 0.11 for extreme quartiles). CONCLUSION: Overall diet quality of 8-year-old children did not conform to dietary guidelines, especially for children having more screen time, children of lower educated or smoking mothers, or from lower-income households.

Determinants of successful lifestyle change during a 6-month preconception lifestyle intervention in women with obesity and infertility.

PURPOSE: To identify demographic, (bio)physical, behavioral, and psychological determinants of successful lifestyle change and program completion by performing a secondary analysis of the intervention arm of a randomized-controlled trial, investigating a preconception lifestyle intervention. METHODS: The 6-month lifestyle intervention consisted of dietary counseling, physical activity, and behavioral modification, and was aimed at 5-10% weight loss. We operationalized successful lifestyle change as successful weight loss (≥ 5% weight/BMI ≤ 29 kg/m2), weight loss in kilograms, a reduction in energy intake, and an increase in physical activity during the intervention program. We performed logistic and mixed-effect regression analyses to identify baseline factors that were associated with successful change or program completion. RESULTS: Women with higher external eating behavior scores had higher odds of successful weight loss (OR 1.10, 95% CI 1.05-1.16). Women with the previous dietetic support lost 0.94 kg less during the intervention period (95% CI 0.01-1.87 kg). Women with higher self-efficacy reduced energy intake more than women with lower self-efficacy (p < 0.01). Women with an older partner had an increased energy intake (6 kcal/year older, 95% CI 3-13). A high stage of change towards physical activity was associated with a higher number of daily steps (p = 0.03). A high stage of change towards weight loss was associated with completion of the intervention (p = 0.04). CONCLUSIONS: Determinants of lifestyle change and program completion were: higher external eating behavior, not having received previous dietetic support, high stage of change. This knowledge can be used to identify women likely to benefit from lifestyle interventions and develop new interventions for women requiring alternative support. TRIAL REGISTRATION: The LIFEstyle study was registered at the Dutch trial registry (NTR 1530; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=1530 ).

Pre-to-post diagnosis weight trajectories in colorectal cancer patients with non-metastatic disease.

PURPOSE: Previous studies have shown that > 50% of colorectal cancer (CRC) patients treated with adjuvant chemotherapy gain weight after diagnosis. This may affect long-term health. Therefore, prevention of weight gain has been incorporated in oncological guidelines for CRC with a focus on patients that undergo adjuvant chemotherapy treatment. It is, however, unknown how changes in weight after diagnosis relate to weight before diagnosis and whether weight changes from pre-to-post diagnosis are restricted to chemotherapy treatment. We therefore examined pre-to-post diagnosis weight trajectories and compared them between those treated with and without adjuvant chemotherapy. METHODS: We included 1184 patients diagnosed with stages I-III CRC between 2010 and 2015 from an ongoing observational prospective study. At diagnosis, patients reported current weight and usual weight 2 years before diagnosis. In the 2 years following diagnosis, weight was self-reported repeatedly. We used linear mixed models to analyse weight trajectories. RESULTS: Mean pre-to-post diagnosis weight change was -0.8 (95% CI -1.1, -0.4) kg. Post-diagnosis weight gain was + 3.5 (95% CI 2.7, 4.3) kg in patients who had lost ≥ 5% weight before diagnosis, while on average clinically relevant weight gain after diagnosis was absent in the groups without pre-diagnosis weight loss. Pre-to-post diagnosis weight change was similar in patients treated with (-0.1 kg (95%CI -0.8, 0.6)) and without adjuvant chemotherapy (-0.9 kg (95%CI -1.4, -0.5)). CONCLUSIONS: Overall, hardly any pre-to-post diagnosis weight change was observed among CRC patients, because post-diagnosis weight gain was mainly observed in patients who lost weight before diagnosis. This was observed independent of treatment with adjuvant chemotherapy.

Importance of details in food descriptions in estimating population nutrient intake distributions.

BACKGROUND: National food consumption surveys are important policy instruments that could monitor food consumption of a certain population. To be used for multiple purposes, this type of survey usually collects comprehensive food information using dietary assessment methods like 24-h dietary recalls (24HRs). However, the collection and handling of such detailed information require tremendous efforts. We aimed to improve the efficiency of data collection and handling in 24HRs, by identifying less important characteristics of food descriptions (facets) and assessing the impact of disregarding them on energy and nutrient intake distributions. METHODS: In the Dutch National Food Consumption Survey 2007-2010, food consumption data were collected through interviewer-administered 24HRs using GloboDiet software in 3819 persons. Interviewers asked participants about the characteristics of each food item according to applicable facets. Food consumption data were subsequently linked to the food composition database. The importance of facets for predicting energy and each of the 33 nutrients was estimated using the random forest algorithm. Then a simulation study was performed to determine the influence of deleting less important facets on population nutrient intake distributions. RESULTS: We identified 35% facets as unimportant and deleted them from the total food consumption database. The majority (79.4%) of the percent difference between percentile estimates of the population nutrient intake distributions before and after facet deletion ranged from 0 to 1%, while 20% cases ranged from 1 to 5% and 0.6% cases more than 10%. CONCLUSION: We concluded that our procedure was successful in identifying less important food descriptions in estimating population nutrient intake distributions. The reduction in food descriptions has the potential to reduce the time needed for conducting interviews and data handling while maintaining the data quality of the survey.

Food identification by barcode scanning in the Netherlands: a quality assessment of labelled food product databases underlying popular nutrition applications.

OBJECTIVE: The quality of labelled food product databases underlying popular diet applications (apps) with barcode scanners was investigated. DESIGN: Product identification rates for the scanned products and the availability and accuracy of nutrient values were calculated. SETTING: One hundred food products were selected from the two largest supermarket chains in the Netherlands. Using the barcode scanners of the selected apps, the products were scanned and the results recorded as food diary entries. The collected data were exported. SUBJECTS: Seven diet apps with barcode scanner and food recording feature were selected from the Google Play and Apple app stores. RESULTS: Energy values were available for 99 % of the scanned products, of which on average 79 % deviated not more than 5 % from the true value. MyFitnessPal provided values for sixteen nutrients, while Virtuagym Food and Yazio provided values for only four nutrients. MyFitnessPal also showed the largest percentage of correctly identified products (i.e. 96 %) and SparkPeople the smallest (i.e. 5 %). The accuracy of the provided nutrient values varied greatly between apps and nutrients. CONCLUSIONS: While energy was the most consistently and accurately reported value, the availability and accuracy of other values varied greatly between apps. Whereas popular diet apps with barcode scanners might be valuable tools for dietary assessments on the product and energy level, they appear less suitable for assessments on the nutrient level. The presence of user-generated database entries implies that the availability of food products might vary depending on the size and diversity of an app's user base.

Using enhanced regression calibration to combine dietary intake estimates from 24 h recall and FFQ reduces bias in diet-disease associations.

OBJECTIVE: 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. SETTING: Wageningen area, the Netherlands, 2011-2013. DESIGN: 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. PARTICIPANTS: Data of 236 individuals collected within the National Dietary Assessment Reference Database. RESULTS: 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. CONCLUSIONS: Our study highlights the potential of combining FFQ and 24hR data. Using RC and ERC resulted in less biased associations for protein and K.

Pre-pregnancy dietary carbohydrate quantity and quality, and risk of developing gestational diabetes: the Australian Longitudinal Study on Women's Health.

Carbohydrate quantity and quality affect postprandial glucose response, glucose metabolism and risk of type 2 diabetes. The aim of this study was to examine the association of pre-pregnancy dietary carbohydrate quantity and quality with the risk of developing gestational diabetes mellitus (GDM). We used data from the Australian Longitudinal Study on Women's Health that included 3607 women aged 25-30 years without diabetes who were followed up between 2003 and 2015. We examined carbohydrate quantity (total carbohydrate intake and a low-carbohydrate diet (LCD) score) and carbohydrate subtypes indicating quality (fibre, total sugar intake, glycaemic index, glycaemic load and intake of carbohydrate-rich food groups). Relative risks (RR) for development of GDM were estimated using multivariable regression models with generalised estimating equations. During 12 years of follow-up, 285 cases of GDM were documented in 6263 pregnancies (4·6 %). The LCD score, reflecting relatively high fat and protein intake and low carbohydrate intake, was positively associated with GDM risk (RR 1·54; 95 % CI 1·10, 2·15), highest quartile v. lowest quartile). Women in the quartile with highest fibre intake had a 33 % lower risk of GDM (RR 0·67; 95 % CI 0·45, 0·96)). Higher intakes of fruit (0·95 per 50 g/d; 95 % CI 0·90, 0·99) and fruit juice (0·89 per 100 g/d; 95 % CI 0·80, 1·00)) were inversely associated with GDM, whereas cereal intake was associated with a higher risk of GDM (RR 1·05 per 20 g/d; 95 % CI 1·01, 1·07)). Thus, a relatively low carbohydrate and high fat and protein intake may increase the risk of GDM, whereas higher fibre intake could decrease the risk of GDM. It is especially important to take the source of carbohydrates into account.

Comparison of meal patterns across five European countries using standardized 24-h recall (GloboDiet) data from the EFCOVAL project.

PURPOSE: To examine meal patterns in terms of frequency and circadian timing of eating in five European countries participating in the EFCOVAL project. METHODS: In this cross-sectional study, 559 men and women, aged 44-65 years, were recruited in Belgium, the Czech Republic, France (Southern part), The Netherlands, and Norway. Dietary data were collected by trained interviewers using standardized computerised 24-h recalls (GloboDiet). Means ± SE of (1) eating frequency, (2) overnight fasting, and (3) time between eating occasions were estimated by country using means from 2 days of 24-h recalls. We also estimated the frequency of eating occasions per hour by country as well as the proportional energy intake of meals/snacks by country compared to the mean energy intake of all countries. RESULTS: Mean eating frequency ranged from 4.3 times/day in France to 7.1 times/day in The Netherlands (p < 0.05). Mean overnight fasting was shortest in the Netherlands (9.2 h) and longest in Czech Republic (10.9 h) (p < 0.05). Mean time between single eating occasions was shortest in The Netherlands (2.4 h) and longest in France (4.3 h) (p < 0.05). Different patterns of energy intake by meals and snacks throughout the day were observed across the five countries. CONCLUSIONS: We observed distinct differences in meal patterns across the five European countries included in the current study in terms of frequency and circadian timing of eating, and the proportion of energy intake from eating occasions.

User-documented food consumption data from publicly available apps: an analysis of opportunities and challenges for nutrition research.

BACKGROUND: 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. METHOD: 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. RESULTS: 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. CONCLUSION: 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.

Validating fatty acid intake as estimated by an FFQ: how does the 24 h recall perform as reference method compared with the duplicate portion?

OBJECTIVE: To compare the performance of the commonly used 24 h recall (24hR) with the more distinct duplicate portion (DP) as reference method for validation of fatty acid intake estimated with an FFQ. DESIGN: Intakes of SFA, MUFA, n-3 fatty acids and linoleic acid (LA) were estimated by chemical analysis of two DP and by on average five 24hR and two FFQ. Plasma n-3 fatty acids and LA were used to objectively compare ranking of individuals based on DP and 24hR. Multivariate measurement error models were used to estimate validity coefficients and attenuation factors for the FFQ with the DP and 24hR as reference methods. SETTING: Wageningen, the Netherlands. SUBJECTS: Ninety-two men and 106 women (aged 20-70 years). RESULTS: Validity coefficients for the fatty acid estimates by the FFQ tended to be lower when using the DP as reference method compared with the 24hR. Attenuation factors for the FFQ tended to be slightly higher based on the DP than those based on the 24hR as reference method. Furthermore, when using plasma fatty acids as reference, the DP showed comparable to slightly better ranking of participants according to their intake of n-3 fatty acids (0·33) and n-3:LA (0·34) than the 24hR (0·22 and 0·24, respectively). CONCLUSIONS: The 24hR gives only slightly different results compared with the distinctive but less feasible DP, therefore use of the 24hR seems appropriate as the reference method for FFQ validation of fatty acid intake.

A national FFQ for the Netherlands (the FFQ-NL1.0): development and compatibility with existing Dutch FFQs.

OBJECTIVE: In the Netherlands, various FFQs have been administered in large cohort studies, which hampers comparison and pooling of dietary data. The present study aimed to describe the development of a standardized Dutch FFQ, FFQ-NL1.0, and assess its compatibility with existing Dutch FFQs. DESIGN: Dutch FFQTOOLTM was used to develop the FFQ-NL1.0 by selecting food items with the largest contributions to total intake and explained variance in intake of energy and thirty-nine nutrients in adults aged 25-69 years from the Dutch National Food Consumption Survey (DNFCS) 2007-2010. Compatibility with the Maastricht-FFQ, Wageningen-FFQ and EPICNL-FFQ was assessed by comparing the number of food items, the covered energy and nutrient intake, and the covered variance in intake. RESULTS: FFQ-NL1.0 comprised 160 food items, v. 253, 183 and 154 food items for the Maastricht-FFQ, Wageningen-FFQ and EPICNL-FFQ, respectively. FFQ-NL1.0 covered ≥85 % of energy and all nutrients reported in the DNFCS. Covered variance in intake ranged from 57 to 99 % for energy and macronutrients, and from 45 to 93 % for micronutrients. Differences between FFQ-NL1.0 and the other FFQs in covered nutrient intake and covered variance in intake were <5 % for energy and all macronutrients. For micronutrients, differences between FFQ-NL and other FFQs in covered level of intake were <15 %, but differences in covered variance were much larger, the maximum difference being 36 %. CONCLUSIONS: The FFQ-NL1.0 was compatible with other FFQs regarding energy and macronutrient intake. However, compatibility for covered variance of intake was limited for some of the micronutrients. If implemented in existing cohorts, it is advised to administer the old and the new FFQ in combination to derive calibration factors.

Systematic Review of Observational Studies with Dose-Response Meta-Analysis between Folate Intake and Status Biomarkers in Adults and the Elderly.

BACKGROUND: Dietary reference values for folate intake vary widely across Europe. METHODS: MEDLINE and Embase through November 2016 were searched for data on the association between folate intake and biomarkers (serum/plasma folate, red blood cell [RBC] folate, plasma homocysteine) from observational studies in healthy adults and elderly. The regression coefficient of biomarkers on intake (ß) was extracted from each study, and the overall and stratified pooled ß and SE (ß) were obtained by random effects meta-analysis on a double log scale. These dose-response estimates may be used to derive folate intake reference values. RESULTS: For every doubling in folate intake, the changes in serum/plasma folate, RBC folate and plasma homocysteine were +22, +21, and -16% respectively. The overall pooled regression coefficients were ß = 0.29 (95% CI 0.21-0.37) for serum/plasma folate (26 estimates from 17 studies), ß = 0.28 (95% CI 0.21-0.36) for RBC (13 estimates from 11 studies), and ß = -0.21 (95% CI -0.31 to -0.11) for plasma homocysteine (10 estimates from 6 studies). CONCLUSION: These estimates along with those from randomized controlled trials can be used for underpinning dietary recommendations for folate in adults and elderly.

Advanced glycation end-products (AGEs) and associations with cardio-metabolic, lifestyle, and dietary factors in a general population: the NQplus study.

BACKGROUND: Advanced glycation end-products are a heterogeneous group of molecules that are formed during reactions between reducing sugars and proteins. Advanced glycation end-products are thought to play a role in several diseases, including diabetes mellitus and can be measured non-invasively using skin autofluorescence (AF). The aim of this cross-sectional study was to investigate associations between skin AF and cardio-metabolic, lifestyle, and dietary factors within a general population. METHODS: The NQplus study is an ongoing longitudinal study in the surroundings of Wageningen, the Netherlands. In this cross-sectional study, skin AF was measured in 957 participants, aged 20 to 77 years, with the AGE Reader. Logistic regression was used to assess associations between skin AF and the following factors: demographics, adiposity, blood lipids, fasting glucose, HbA1c , blood pressure, dietary intake, and disease history. Stepwise linear regression was used to identify factors correlating with skin AF. RESULTS: In males, skin AF was significantly associated with age, coffee intake, systolic and diastolic blood pressure, body fat, full fat dairy, and educational level. In females, significant associations were found with age, coffee intake, HbA1c , diabetes, and eGFR. In the total population, current smoking was positively associated with skin AF. CONCLUSIONS: This cross-sectional study in a general population showed that age and coffee intake were associated with skin AF in both males and females. We observed a gender disparity for some of the associations with skin AF, which need to be confirmed by further research. More detailed research is needed to assess the association between skin AF and diet.

Self-reported eating rate is associated with weight status in a Dutch population: a validation study and a cross-sectional study.

BACKGROUND: 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. METHODS: 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/m2) 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/m2) using (multiple) linear regression analysis. RESULTS: 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/m2 (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/m2 (95% CI -0.22, 0.80) higher BMI compared to average-speed-eating men, after adjusting for confounders. CONCLUSIONS: 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.