Results: Overall, 5857 men had a weight gain of 5 kg or greater during 10 years of follow-up. Breakfast consumption was inversely associated with the risk of 5-kg weight gain after adjustment for age [hazard ratio (HR) ϭ 0.77 (95% confidence interval [CI], 0.72 to 0.82)], and this association was independent of lifestyle and BMI at baseline [HR ϭ 0.87 (95% CI, 0.82 to 0.93)]. Fiber and nutrient intakes partially explained the association between breakfast consumption and weight gain. The inverse association between breakfast consumption and weight gain was more pronounced in men with a baseline BMI of 25 kg/m 2 or lower [multivariate HR ϭ 0.78 (95% CI, 0.70 to 0.87)] than in men who were overweight at baseline [HR ϭ 0.92 (95% CI, 0.85 to 1.00)]. Furthermore, we observed that an increasing number of eating occasions in addition to three standard meals was associated with a higher risk of 5-kg weight gain [HR ϭ 1.15 (95% CI, 1.06 to 1.25, for Ն2 vs. 0 additional eating occasions)]. Discussion: These findings suggest that the consumption of breakfast may modestly contribute to the prevention of weight gain as compared with skipping breakfast in middleaged and older men.

Background: Dairy consumption has been postulated to reduce the risk of obesity and metabolic disturbances. Objective: The aim of this study was to evaluate the associations of dairy consumption with body weight and other components of the metabolic syndrome. Design: We used cross-sectional data for 2064 men and women aged 50 -75 y who participated in the Hoorn Study. The metabolic syndrome was defined according to the National Cholesterol Education Program Expert Panel. Dairy consumption was assessed by using a semiquantitative food-frequency questionnaire. Results: The median consumption of total dairy products was 4.1 servings/d. After adjustment for potential confounders (ie, dietary factors, physical activity, smoking, income, educational level, and antihypertensive medication), total dairy consumption was significantly associated with lower diastolic blood pressure (␤ Ȁ SE: Ҁ0.31 Ȁ 0.12 mm Hg/serving) and higher fasting glucose concentrations (0.04 Ȁ 0.02 mmol/L per serving), but not with body weight or other metabolic variables (ie, lipids, postload glucose, or insulin). When different dairy products were distinguished, borderline significant (P 0.10) inverse associations were observed for dairy desserts, milk, and yogurt with systolic (Ҁ1.26 Ȁ 0.58, Ҁ0.57 Ȁ 0.34, and Ҁ1.28 Ȁ 0.74 mm Hg/serving, respectively) and diastolic (Ҁ0.58 Ȁ 0.31, Ҁ0.57 Ȁ 0.18, and Ҁ0.35 Ȁ 0.40 mm Hg/serving, respectively) blood pressure, whereas cheese consumption was positively associated with body mass index (0.15 Ȁ 0.08/serving). Conclusion:In an elderly Dutch population, higher dairy consumption was not associated with lower weight or more favorable levels of components of the metabolic syndrome, except for a modest association with lower blood pressure.Am J Clin Nutr 2007;85: 989 -95.

Four subgroups with distinct HbA1c trajectories were identified. More than 90 % reached and maintained good glycemic control (subgroup one and two). Patients within the two subgroups that showed a more unfavorable course of glycemic control were younger, had higher HbA1c levels and a longer diabetes duration at baseline.

A large reduction in retinopathy screening was achieved using the model in this population of patients with a very low incidence of retinopathy. Considering the number of potentially missed cases of STR, there is room for improvement in the model. Use of the model for personalised screening may eventually help to reduce healthcare use and costs of diabetes care.

Objectives To identify and assess the quality and accuracy of prognostic models for nephropathy and to validate these models in external cohorts of people with type 2 diabetes. Design Systematic review and external validation. Data sources PubMed and Embase. Eligibility criteria Studies describing the development of a model to predict the risk of nephropathy, applicable to people with type 2 diabetes. Methods Screening, data extraction, and risk of bias assessment were done in duplicate. Eligible models were externally validated in the Hoorn Diabetes Care System (DCS) cohort (n=11 450) for the same outcomes for which they were developed. Risks of nephropathy were calculated and compared with observed risk over 2, 5, and 10 years of follow-up. Model performance was assessed based on intercept adjusted calibration and discrimination (Harrell’s C statistic). Results 41 studies included in the systematic review reported 64 models, 46 of which were developed in a population with diabetes and 18 in the general population including diabetes as a predictor. The predicted outcomes included albuminuria, diabetic kidney disease, chronic kidney disease (general population), and end stage renal disease. The reported apparent discrimination of the 46 models varied considerably across the different predicted outcomes, from 0.60 (95% confidence interval 0.56 to 0.64) to 0.99 (not available) for the models developed in a diabetes population and from 0.59 (not available) to 0.96 (0.95 to 0.97) for the models developed in the general population. Calibration was reported in 31 of the 41 studies, and the models were generally well calibrated. 21 of the 64 retrieved models were externally validated in the Hoorn DCS cohort for predicting risk of albuminuria, diabetic kidney disease, and chronic kidney disease, with considerable variation in performance across prediction horizons and models. For all three outcomes, however, at least two models had C statistics >0.8, indicating excellent discrimination. In a secondary external validation in GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland), models developed for diabetic kidney disease outperformed those for chronic kidney disease. Models were generally well calibrated across all three prediction horizons. Conclusions This study identified multiple prediction models to predict albuminuria, diabetic kidney disease, chronic kidney disease, and end stage renal disease. In the external validation, discrimination and calibration for albuminuria, diabetic kidney disease, and chronic kidney disease varied considerably across prediction horizons and models. For each outcome, however, specific models showed good discrimination and calibration across the three prediction horizons, with clinically accessible predictors, making them applicable in a clinical setting. Systematic review registration PROSPERO CRD42020192831.

Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/).

OBJECTIVETo investigate whether women with type 2 diabetes (T2D) develop a more advanced stage of breast cancer and whether treatment with insulin (analogs) is associated with specific breast cancer characteristics. RESEARCH DESIGN AND METHODSFor this nested case-control study, women with breast cancer diagnosed in 2002-2014 were selected from the linked Netherlands Cancer Registry-PHARMO Database Network (N = 33,377). T2D was defined as receiving two or more dispensings of noninsulin blood glucose-lowering drugs prior to breast cancer diagnosis. Women with T2D were matched to women without diabetes. Among women with T2D, insulin users and nonusers were compared. Multivariable ordinal logistic regression was used to investigate the association between T2D/insulin and breast cancer characteristics, including TNM classification (tumor size, lymph node status, metastasis), morphology, grade, estrogen receptor and progesterone receptor (PR), human epidermal growth factor receptor 2, and molecular subtype. RESULTSWomen with T2D (n = 1,567) were more often diagnosed with a more advanced tumor stage (odds ratio 1.28 [95% CI 13-1.44]) and a higher grade (1.22 [1.08-1.39]) though less often with a PR-negative breast tumor (0.77 [0.67-0.89]) than women without diabetes (n = 6,267). No associations were found for the other breast cancer characteristics. Women with T2D using insulin (n = 388) were not diagnosed with different breast cancer characteristics compared with women with T2D not using insulin (n = 1,179). CONCLUSIONSOur study suggests that women with T2D are at increased risk to be diagnosed with a more aggressive type of breast cancer than women without diabetes. No evidence was found that the use of insulin (analogs) is associated with developing more advanced breast cancer tumors.

Background Drug-related problems (DRP) following hospital discharge may cause morbidity, mortality and hospital re-admissions. It is unclear whether a clinical medication review (CMR) and counseling at discharge is a cost-effective method to reduce DRP. Objective To assess the effect of a CMR on health care utilization and to investigate whether CMR is a cost-effective method to reduce DRP in older polypharmacy patients discharged from hospital. Setting 24 community pharmacies in the Netherlands. Method A cluster-randomized controlled trial with an economic evaluation. Community pharmacies were randomized to those providing a CMR, counseling and follow-up at discharge and those providing usual care. Main outcome measures Change in the number of DRP after 1 year of follow-up and costs of health care utilization during follow-up. In 216 patients the use of health care was prospectively assessed. Missing data on effects and costs were imputed using multiple imputation techniques. Bootstrapping techniques were used to estimate the uncertainty around the differences in costs and incremental cost-effectiveness ratios. Results CMR resulted in a small reduction of DRP. The proportion of patients readmitted to the hospital during 6 months of follow-up was significantly higher in the intervention group than in the control group (46.4 vs. 20.9%; p < 0.05). Health care costs were higher in the intervention group, although not statistically significant. The costs of reducing one DRP by a CMR amounted to €8270. Conclusion A CMR in vulnerable older patients at hospital discharge led to a small reduction in DRP. Because of a significantly higher use of health care and higher number of re-hospitalisations post CMR, the present study data indicate that performing the intervention in this patient population is not cost-effective.