Dairy shows different associations with abdominal and BMI-defined overweight: Cross-sectional analyses exploring a variety of dairy products.

BACKGROUND AND AIMS: Previous studies have suggested weight-regulatory properties for several dairy nutrients, but population-based studies on dairy and body weight are inconclusive. We explored cross-sectional associations between dairy consumption and indicators of overweight. METHODS AND RESULTS: We included 114,682 Dutch adults, aged ≥18 years. Dairy consumption was quantified by a food frequency questionnaire. Abdominal overweight was defined as waist circumference (WC) ≥88 cm (women) or ≥102 cm (men) (n = 37,391), overweight as BMI ≥25-30 kg/m2 (n = 44,772) and obesity as BMI ≥30 kg/m2 (n = 15,339). Associations were quantified by logistic (abdominal overweight, no/yes), multinomial logistic (BMI-defined overweight and obesity) and linear regression analyses (continuous measures of WC and BMI), and they were adjusted for relevant covariates. Total dairy showed a positive association with abdominal overweight (OR Q1 ref vs. Q5: 1.09; 95% CI: 1.04-1.14) and with BMI-defined overweight (OR Q5 1.13; 95% CI: 1.08-1.18) and obesity (OR Q5 1.09; 95% CI: 1.02-1.16). Skimmed, semi-skimmed and non-fermented dairy also showed positive associations with overweight categories. Full-fat dairy showed an inverse association with overweight and obesity (OR Q5 for obesity: 0.78; 95% CI: 0.73-0.83). Moreover, inverse associations were observed for yoghurt and custard and positive associations for milk, buttermilk, flavoured yoghurt drinks, cheese and cheese snacks. Fermented dairy, curd cheese and Dutch cheese did not show a consistent association with overweight categories. CONCLUSIONS: Total, skimmed, semi-skimmed and non-fermented dairy; milk; buttermilk; flavoured yoghurt drinks; total cheese and cheese snacks showed a positive association with overweight categories, whereas full-fat dairy, custard and yoghurt showed an inverse association with overweight categories.

Associations between dietary factors and markers of NAFLD in a general Dutch adult population.

BACKGROUND/OBJECTIVES: The objective of this sudy was to assess the relationship between dietary intake and fatty liver as scored by the validated Fatty Liver Index (FLI) in a large cross-sectional study among a general Dutch adult population. Diet is known to affect liver fat accumulation in humans. SUBJECTS/METHODS: 1128 men and women aged 20-70 years were included. Dietary intake was assessed using a validated semiquantitative food frequency questionnaire. FLI was derived from body mass index (BMI), waist circumference, triglycerides and gamma-glutamyltransferase. Associations were adjusted for energy intake, alcohol intake, age, sex, education, smoking and prevalence of hypertension and diabetes. RESULTS: In this population (mean age 53.0±11.4 years; BMI 25.9±4.0 kg/m2; FLI 35.0±27.7), the prevalence of fatty liver as indicated by an FLI>60 was 21.5%. Subjects in the highest FLI category were more likely to be male, older and less physically active. Total protein intake and animal protein intake were positively associated with the highest FLI score versus the lowest (odds ratio (OR) 1.25 per 1 en%, 95% confidence interval (CI) 1.15-1.37 and OR 1.27, 95% CI 1.17-1.38, respectively); for vegetable protein, an inverse association was observed (OR 0.81, 95% CI 0.69-0.94). A similar positive association with FLI was observed when carbohydrates and fat were iso-calorically exchanged for total and animal proteins. CONCLUSIONS: Subjects in the high FLI group consumed more protein, especially from animal origin, less carbohydrates and less dietary fibre. The presence of fatty liver was associated with a higher intake of animal protein and total fat, soft drinks and snacks.

Alcoholic beverage preference and diabetes incidence across Europe: the Consortium on Health and Ageing Network of Cohorts in Europe and the United States (CHANCES) project.

BACKGROUND/OBJECTIVES: It is unknown if wine, beer and spirit intake lead to a similar association with diabetes. We studied the association between alcoholic beverage preference and type 2 diabetes incidence in persons who reported to consume alcohol. SUBJECTS/METHODS: Ten European cohort studies from the Consortium on Health and Ageing: Network of Cohorts in Europe and the United States were included, comprising participant data of 62 458 adults who reported alcohol consumption at baseline. Diabetes incidence was based on documented and/or self-reported diagnosis during follow-up. Preference was defined when ⩾70% of total alcohol consumed was either beer, wine or spirits. Adjusted hazard ratios (HRs) were computed using Cox proportional hazard regression. Single-cohort HRs were pooled by random-effects meta-analysis. RESULTS: Beer, wine or spirit preference was not related to diabetes risk compared with having no preference. The pooled HRs were HR 1.06 (95% confidence interval (CI) 0.93, 1.20) for beer, HR 0.99 (95% CI 0.88, 1.11) for wine, and HR 1.19 (95% CI 0.97, 1.46) for spirit preference. Absolute wine intake, adjusted for total alcohol, was associated with a lower diabetes risk: pooled HR per 6 g/day was 0.96 (95% CI 0.93, 0.99). A spirit preference was related to a higher diabetes risk in those with a higher body mass index, in men and women separately, but not after excluding persons with prevalent diseases. CONCLUSIONS: This large individual-level meta-analysis among persons who reported alcohol consumption revealed that the preference for beer, wine, and spirits was similarly associated with diabetes incidence compared with having no preference.

Fructose consumption in the Netherlands: the Dutch National Food Consumption Survey 2007-2010.

BACKGROUND/OBJECTIVES: Despite the worldwide scientific and media attention, the actual fructose consumption in many non-US populations is not clear. The aim of this study was to estimate the fructose consumption and its main food sources in a representative sample of the general Dutch population. SUBJECTS/METHODS: In all, 3817 children and adults aged 7-69 years from the Dutch National Food Consumption Survey 2007-2010 were studied. Values for fructose content of the products were assigned using several food composition tables. Diet was assessed with two nonconsecutive 24-h dietary recalls. The Multiple Source Method was used to take into account day-to-day variation and to estimate the habitual fructose consumption. RESULTS: Median habitual fructose intake was 46 g/day, with an interquartile range of 35-60 g/day. In boys, the highest median intake was observed among 14- to 18-year olds: 61 g/day. In girls, those aged 9-13 years reported the highest median intake: 56 g/day. Of total fructose intake, 67% was consumed in the form of sucrose and 33% was consumed as free fructose. Soft drinks constituted the main food source of total fructose (13-29% across age and sex categories), followed by juices (9-12%), fruit (9-18%), cake and cookies (9-11%) and dairy products (6-10%). CONCLUSIONS: Fructose comprised 9% of the mean daily energy intake in the general Dutch population aged 7-69 years. The fructose consumption was somewhat lower than most recent figures from the US. The main food sources of fructose were soft drinks, juices and fruit.

Nutrient-rich foods, cardiovascular diseases and all-cause mortality: the Rotterdam study.

BACKGROUND/OBJECTIVES: The nutrient-rich food (NRF) index assesses nutrient quality of individual food items by ranking them according to their nutrient composition. The index reflects the nutrient density of the overall diet. We examined the associations between the NRF9.3 index-a score on the basis of nine beneficial nutrients (protein, fiber, vitamins and minerals) and three nutrients to limit (saturated fat, sugar and sodium)-incidence of cardiovascular disease (CVD) events and all-cause mortality. SUBJECTS/METHODS: A total of 4969 persons aged 55 and older from the Rotterdam Study, a prospective cohort study in the Netherlands, were studied. First, all foods were scored on the basis of their nutrient composition, resulting in an NRF9.3 score on food item level. Subsequently, they were converted into individual weighted scores on the basis of the amount of calories of each food item consumed by the subjects and the total energy intake. The hazard ratios (HRs) of the NRF9.3 index score were adjusted for age, gender, body mass index, smoking history, doctor-prescribed diet, alcohol consumption and education. RESULTS: Food groups that contributed most to the NRF9.3 index score were vegetables, milk and milk products, fruit, bread and potatoes. A high NRF9.3 index score was inversely associated with all-cause mortality (HR Q4 versus Q1: 0.84 (95% confidence interval: 0.74, 0.96)). Associations were stronger in women than in men. The NRF9.3 index score was not associated with incidence of CVD. CONCLUSION: Elderly with a higher NRF9.3 index score, indicating more beneficial components and/or less limiting components, had a lower risk of all-cause mortality. Consuming a nutrient-dense diet may improve survival.

Alcoholic beverage preference and diet in a representative Dutch population: the Dutch national food consumption survey 2007-2010.

BACKGROUND/OBJECTIVES: The habitual consumption of a specific type of alcoholic beverage may be related to the overall dietary pattern. The objective of this cross-sectional study was to investigate associations between alcoholic beverage preference and dietary intake in The Netherlands. SUBJECTS/METHODS: A total of 2100 men and women from the Dutch National Food Consumption Survey 2007-2010 were studied. A general questionnaire assessed alcoholic beverage preference and two non-consecutive 24-h dietary recalls assessed overall diet. Mean nutrient and food group intakes, and adherence to the 2006 Dutch dietary guidelines across categories of alcoholic beverage preference were compared and adjusted for age, sex, body mass index (BMI), education, smoking, physical activity, energy intake and frequency and absolute alcohol consumption. RESULTS: Largest differences in dietary habits were detected between persons who preferred wine and those who preferred beer. Persons with a beer preference had a higher absolute intake of meat, soft drinks, margarine and snacks. In contrast, persons with a wine preference had a higher absolute consumption of healthy foods. However, after multiple adjustments, wine consumers still consumed less energy and more vegetables and fruit juices compared with beer consumers. Adherence to the Dutch dietary guidelines did not differ between preference categories after multiple adjustments. CONCLUSIONS: In this cross-sectional analysis in a representative sample of the Dutch population, a beer preference was associated with less healthy dietary behaviour, especially compared with wine preference. However, these differences were largely explained by other socio-demographic and lifestyle factors. These results suggest that alcoholic beverage preference may not be independently related to diet.

Diabetes mellitus, glycated haemoglobin and C-peptide levels in relation to pancreatic cancer risk: a study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort.

AIMS/HYPOTHESIS: There has been long-standing debate about whether diabetes is a causal risk factor for pancreatic cancer or a consequence of tumour development. Prospective epidemiological studies have shown variable relationships between pancreatic cancer risk and blood markers of glucose and insulin metabolism, overall and as a function of lag times between marker measurements (blood donation) and date of tumour diagnosis. METHODS: Pre-diagnostic levels of HbA(1c) and C-peptide were measured for 466 participants with pancreatic cancer and 466 individually matched controls within the European Prospective Investigation into Cancer and Nutrition. Conditional logistic regression models were used to estimate ORs for pancreatic cancer. RESULTS: Pancreatic cancer risk gradually increased with increasing pre-diagnostic HbA(1c) levels up to an OR of 2.42 (95% CI 1.33, 4.39 highest [≥ 6.5%, 48 mmol/mol] vs lowest [≤ 5.4%, 36 mmol/mol] category), even for individuals with HbA(1c) levels within the non-diabetic range. C-peptide levels showed no significant relationship with pancreatic cancer risk, irrespective of fasting status. Analyses showed no clear trends towards increasing hyperglycaemia (as marked by HbA(1c) levels) or reduced pancreatic beta cell responsiveness (as marked by C-peptide levels) with decreasing time intervals from blood donation to cancer diagnosis. CONCLUSIONS/INTERPRETATION: Our data on HbA(1c) show that individuals who develop exocrine pancreatic cancer tend to have moderate increases in HbA(1c) levels, relatively independently of obesity and insulin resistance-the classic and major risk factors for type 2 diabetes. While there is no strong difference by lag time, more data are needed on this in order to reach a firm conclusion.

Food intake of individuals with and without diabetes across different countries and ethnic groups.

BACKGROUND/OBJECTIVES: Given the importance of nutrition therapy in diabetes management, we hypothesized that food intake differs between individuals with and without diabetes. We investigated this hypothesis in two large prospective studies including different countries and ethnic groups. SUBJECTS/METHODS: Study populations were the European Prospective Investigation into Cancer and Nutrition Study (EPIC) and the Multiethnic Cohort Study (MEC). Dietary intake was assessed by food frequency questionnaires, and calibrated using 24h-recall information for the EPIC Study. Only confirmed self-reports of diabetes at cohort entry were included: 6192 diabetes patients in EPIC and 13 776 in the MEC. For the cross-sectional comparison of food intake and lifestyle variables at baseline, individuals with and without diabetes were matched 1:1 on sex, age in 5-year categories, body mass index in 2.5 kg/m(2) categories and country. RESULTS: Higher intake of soft drinks (by 13 and 44% in the EPIC and MEC), and lower consumption of sweets, juice, wine and beer (>10% difference) were observed in participants with diabetes compared with those without. Consumption of vegetables, fish and meat was slightly higher in individuals with diabetes in both studies, but the differences were <10%. Findings were more consistent across different ethnic groups than countries, but generally showed largely similar patterns. CONCLUSIONS: Although diabetes patients are expected to undergo nutritional education, we found only small differences in dietary behavior in comparison with cohort members without diabetes. These findings suggest that emphasis on education is needed to improve the current behaviors to assist in the prevention of complications.

External validation of the UK Prospective Diabetes Study (UKPDS) risk engine in patients with type 2 diabetes.

AIMS/HYPOTHESIS: Treatment guidelines recommend the UK Prospective Diabetes Study (UKPDS) risk engine for predicting cardiovascular risk in patients with type 2 diabetes, although validation studies showed moderate performance. The methods used in these validation studies were diverse, however, and sometimes insufficient. Hence, we assessed the discrimination and calibration of the UKPDS risk engine to predict 4, 5, 6 and 8 year cardiovascular risk in patients with type 2 diabetes. METHODS: The cohort included 1,622 patients with type 2 diabetes. During a mean follow-up of 8 years, patients were followed for incidence of CHD and cardiovascular disease (CVD). Discrimination and calibration were assessed for 4, 5, 6 and 8 year risk. Discrimination was examined using the c-statistic and calibration by visually inspecting calibration plots and calculating the Hosmer-Lemeshow χ(2) statistic. RESULTS: The UKPDS risk engine showed moderate to poor discrimination for both CHD and CVD (c-statistic of 0.66 for both 5 year CHD and CVD risks), and an overestimation of the risk (224% and 112%). The calibration of the UKPDS risk engine was slightly better for patients with type 2 diabetes who had been diagnosed with diabetes more than 10 years ago compared with patients diagnosed more recently, particularly for 4 and 5 year predicted CVD and CHD risks. Discrimination for these periods was still moderate to poor. CONCLUSIONS/INTERPRETATION: We observed that the UKPDS risk engine overestimates CHD and CVD risk. The discriminative ability of this model is moderate, irrespective of various subgroup analyses. To enhance the prediction of CVD in patients with type 2 diabetes, this model should be updated.

Non-fasting lipids and risk of cardiovascular disease in patients with diabetes mellitus.

AIMS/HYPOTHESIS: The aim of this study was to examine the effect of postprandial time on the associations and predictive value of non-fasting lipid levels and cardiovascular disease risk in participants with diabetes. METHODS: This study was conducted among 1,337 participants with diabetes from the Dutch and German (Potsdam) contributions to the European Prospective Investigation into Cancer and Nutrition. At baseline, total cholesterol, LDL- and HDL-cholesterol and triacylglycerol concentrations were measured and the ratio of total cholesterol/HDL-cholesterol was calculated. Participants were followed for incidence of cardiovascular disease. RESULTS: Lipid concentrations changed minimally with increasing postprandial time, except for triacylglycerol which was elevated just after a meal and declined over time (1.86 at 0.1 h to 1.33 at >6 h, p for trend <0.001). During a mean follow-up of 8 years, 116 cardiovascular events were documented. After adjustment for potential confounders, triacylglycerol (HR for third tertile compared with first tertile (HR(t)3(to)1), 1.73 [95% CI 1.04, 2.87]), HDL-cholesterol (HR(t)3(to)1, 0.41 [95% CI 0.23, 0.72]) and total cholesterol/HDL-cholesterol ratio (HR(t)3(to)1, 1.65 [95% CI 0.95, 2.85]) were associated with cardiovascular disease, independent of postprandial time. Cardiovascular disease risk prediction using the UK Prospective Diabetes Study risk engine was not affected by postprandial time. CONCLUSIONS/INTERPRETATION: Postprandial time did not affect associations between lipid concentrations and cardiovascular disease risk in patients with diabetes, nor did it influence prediction of cardiovascular disease. Therefore, it may not be necessary to use fasting blood samples to determine lipid concentrations for cardiovascular disease risk prediction in patients with diabetes.