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1.  Associations of alcoholic beverage preference with cardiometabolic and lifestyle factors: the NQplus study 
BMJ Open  2016;6(6):e010437.
The preference for a specific alcoholic beverage may be related to an individual's overall lifestyle and health. The objective was to investigate associations between alcoholic beverage preference and several cardiometabolic and lifestyle factors, including adiposity, cholesterol, glycated haemoglobin (HbA1c), liver enzymes and dietary patterns.
Cross-sectional study.
The Dutch Longitudinal Nutrition Questionnaires plus (NQplus) Study.
1653 men and women aged 20–77 years.
Diet, including alcohol, was assessed by Food Frequency Questionnaire. Based on the average number of reported glasses of alcoholic beverage, a person was classified as having a preference for beer, wine, spirit/no specific preference, or as a non-consumer. Mixed linear models were used to calculate crude and adjusted means of cardiometabolic and lifestyle factors across alcoholic beverage preference categories.
Primary outcome measures
Anthropometric measures, blood pressure, lipids, HbA1c, albumin, creatinine, uric acid, liver enzymes and dietary patterns.
In the study population, 43% had a wine preference, 13% a beer preference, 29% had a spirit or no specific preference, and 15% did not consume alcohol. Men who preferred wine had lowest measures of adiposity; the preference for alcoholic beverages was not associated with adiposity measures in women. Wine consumers had higher high density lipoprotein-cholesterol, lower HbA1c and were more likely to follow the ‘Salad’ pattern. Beer consumers had highest levels of triglycerides and liver enzymes, and had higher scores for the ‘Meat’ and ‘Bread’ pattern.
Few differences in dietary patterns across alcoholic beverage preference categories were observed. Those differences in cardiometabolic parameters that were observed according to alcoholic beverage preference, suggested that wine consumers have a better health status than beer consumers.
PMCID: PMC4916604  PMID: 27311903
alcoholic beverage preference; beer; wine; dietary patterns; cardio-metabolic risk factors
3.  Socio-economic status and ethnicity are independently associated with dietary patterns: the HELIUS-Dietary Patterns study 
Food & Nutrition Research  2015;59:10.3402/fnr.v59.26317.
Differences in dietary patterns between ethnic groups have often been observed. These differences may partially be a reflection of differences in socio-economic status (SES) or may be the result of differences in the direction and strength of the association between SES and diet.
We aimed to examine ethnic differences in dietary patterns and the role of socio-economic indicators on dietary patterns within a multi-ethnic population.
Cross-sectional multi-ethnic population-based study.
Amsterdam, the Netherlands.
Principal component analysis was used to identify dietary patterns among Dutch (n=1,254), South Asian Surinamese (n=425), and African Surinamese (n=784) participants. Levels of education and occupation were used to indicate SES. Linear regression analysis was used to examine the association between ethnicity and dietary pattern scores first and then between socio-economic indicators and dietary patterns within and between ethnic groups.
‘Noodle/rice dishes and white meat’, ‘red meat, snacks, and sweets’ and ‘vegetables, fruit and nuts’ patterns were identified. Compared to the Dutch origin participants, Surinamese more closely adhered to the ‘noodle/rice dishes and white meat’ pattern which was characterized by foods consumed in a ‘traditional Surinamese diet’. Closer adherence to the other two patterns was observed among Dutch compared to Surinamese origin participants. Ethnic differences in dietary patterns persisted within strata of education and occupation. Surinamese showed greater adherence to a ‘traditional’ pattern independent of SES. Among Dutch participants, a clear socio-economic gradient in all dietary patterns was observed. Such a gradient was only present among Surinamese dietary oatterns to the ‘vegetables, fruit and nuts’ pattern.
We found a selective change in the adherence to dietary patterns among Surinamese origin participants, presumably a move towards more vegetables and fruits with higher SES but continued fidelity to the traditional diet.
PMCID: PMC4454783  PMID: 26041009
dietary patterns; non-Western ethnic minority groups; education; occupation; socio-economic status; HELIUS study
4.  The Dutch Healthy Diet index as assessed by 24 h recalls and FFQ: associations with biomarkers from a cross-sectional study 
The Dutch Healthy Diet index (DHD-index) was developed using data from two 24 h recalls (24hR) and appeared useful to evaluate diet quality in Dutch adults. As many epidemiologic studies use FFQ, we now estimated the DHD-index score using FFQ data. We compared whether this score showed similar associations with participants' characteristics, micronutrient intakes, and biomarkers of intake and metabolism compared with the DHD-index using 24hR data. Data of 121 Dutch participants of the European Food Consumption Validation study were used. Dietary intake was assessed by two 24hR and a 180-item FFQ. Biomarkers measured were serum total cholesterol and carotenoids, EPA + DHA in plasma phospholipids and 24 h urinary Na. A correlation of 0·48 (95 % CI 0·33, 0·61) was observed between the DHD-index score based on 24hR data and on FFQ data. Classification of participants into the same tertiles of the DHD-index was achieved for 57 %. Women showed higher DHD-index scores. Energy intake was inversely associated with both DHD-index scores. Furthermore, age and intakes of folate, Fe, Mg, K, vitamin B6 and vitamin C were positively associated with both DHD-index scores. DHD-index scores showed acceptable correlations with the four combined biomarkers taking energy intake into account (r24hR 0.55; rFFQ 0.51). In conclusion, the DHD-index score based on FFQ data shows similar associations with participants' characteristics, energy intake, micronutrient intake and biomarkers compared with the score based on 24hR data. Furthermore, ranking of participants was acceptable for both methods. FFQ data may therefore be used to assess diet quality using the DHD-index in Dutch populations.
PMCID: PMC4153287  PMID: 25191596
Dutch Healthy Diet index; Dietary patterns; Biomarkers; Dietary assessment methods; 24hR, 24 h recall; ADF, acidic drink and food; DHD-index, Dutch Healthy Diet index; DNFCS-2003, Dutch National Food Consumption Survey of 2003; EFCOVAL, European Food Consumption Validation; TFA, trans-fatty acid
5.  Lifestyle Counseling for Type 2 Diabetes Risk Reduction in Dutch Primary Care 
Diabetes Care  2011;34(9):1919-1925.
To study the overall effect of the Active Prevention in High-Risk Individuals of Diabetes Type 2 in and Around Eindhoven (APHRODITE) lifestyle intervention on type 2 diabetes risk reduction in Dutch primary care after 0.5 and 1.5 years and to evaluate the variability between general practices.
Individuals at high risk for type 2 diabetes (Finnish Diabetes Risk Score ≥13) were randomly assigned into an intervention group (n = 479) or a usual-care group (n = 446). Comparisons were made between study groups and between general practices regarding changes in clinical and lifestyle measures over 1.5 years. Participant, general practitioner, and nurse practitioner characteristics were compared between individuals who lost weight or maintained a stable weight and individuals who gained weight.
Both groups showed modest changes in glucose values, weight measures, physical activity, energy intake, and fiber intake. Differences between groups were significant only for total physical activity, saturated fat intake, and fiber intake. Differences between general practices were significant for BMI and 2-h glucose but not for energy intake and physical activity. In the intervention group, the nurse practitioners’ mean years of work experience was significantly longer in individuals who were successful at losing weight or maintaining a stable weight compared with unsuccessful individuals. Furthermore, successful individuals more often had a partner.
Risk factors for type 2 diabetes could be significantly reduced by lifestyle counseling in Dutch primary care. The small differences in changes over time between the two study groups suggest that additional intervention effects are modest. In particular, the level of experience of the nurse practitioner and the availability of partner support seem to facilitate intervention success.
PMCID: PMC3161269  PMID: 21775759
6.  The Dutch Healthy Diet index (DHD-index): an instrument to measure adherence to the Dutch Guidelines for a Healthy Diet 
Nutrition Journal  2012;11:49.
The objective was to develop an index based on the Dutch Guidelines for a healthy Diet of 2006 that reflects dietary quality and to apply it to the Dutch National Food Consumption Survey (DNFCS) to examine the associations with micronutrient intakes.
A total of 749 men and women, aged 19–30 years, contributed two 24-hour recalls and additional questionnaires in the DNFCS of 2003. The Dutch Healthy Diet index (DHD-index) includes ten components representing the ten Dutch Guidelines for a Healthy Diet. Per component the score ranges between zero and ten, resulting in a total score between zero (no adherence) and 100 (complete adherence).
The mean ± SD of the DHD-index was 60.4 ± 11.5 for women and 57.8 ± 10.8 for men (P for difference = 0.002). Each component score increased across the sex-specific quintiles of the DHD-index. An inverse association was observed between the sex-specific quintiles of the DHD-index and total energy intake. Calcium, riboflavin, and vitamin E intake decreased with increasing DHD-index, an inverse association which disappeared after energy adjustment. Vitamin C showed a positive association across quintiles, also when adjusted for energy. For folate, iron, magnesium, potassium, thiamin, and vitamin B6 a positive association emerged after adjustment for energy.
The DHD-index is capable of ranking participants according to their adherence to the Dutch Guidelines for a Healthy Diet by reflecting variation in nine out of ten components that constitute the index when based on two 24-hour recalls. Furthermore, the index showed to be a good measure of nutrient density of diets.
PMCID: PMC3439289  PMID: 22818824
The Netherlands; Index; Dietary patterns; Dietary guidelines
7.  Bias in protein and potassium intake collected with 24-h recalls (EPIC-Soft) is rather comparable across European populations 
European Journal of Nutrition  2011;51(8):997-1010.
We investigated whether group-level bias of a 24-h recall estimate of protein and potassium intake, as compared to biomarkers, varied across European centers and whether this was influenced by characteristics of individuals or centers.
The combined data from EFCOVAL and EPIC studies included 14 centers from 9 countries (n = 1,841). Dietary data were collected using a computerized 24-h recall (EPIC-Soft). Nitrogen and potassium in 24-h urine collections were used as reference method. Multilevel linear regression analysis was performed, including individual-level (e.g., BMI) and center-level (e.g., food pattern index) variables.
For protein intake, no between-center variation in bias was observed in men while it was 5.7% in women. For potassium intake, the between-center variation in bias was 8.9% in men and null in women. BMI was an important factor influencing the biases across centers (p < 0.01 in all analyses). In addition, mode of administration (p = 0.06 in women) and day of the week (p = 0.03 in men and p = 0.06 in women) may have influenced the bias in protein intake across centers. After inclusion of these individual variables, between-center variation in bias in protein intake disappeared for women, whereas for potassium, it increased slightly in men (to 9.5%). Center-level variables did not influence the results.
The results suggest that group-level bias in protein and potassium (for women) collected with 24-h recalls does not vary across centers and to a certain extent varies for potassium in men. BMI and study design aspects, rather than center-level characteristics, affected the biases across centers.
Electronic supplementary material
The online version of this article (doi:10.1007/s00394-011-0279-z) contains supplementary material, which is available to authorized users.
PMCID: PMC3496541  PMID: 22143464
Diet; Protein; Potassium; Biomarker; Validity; 24-h dietary recall; Multilevel

Results 1-7 (7)