Multivalent grafting of hyperbranched oligo- and polyglycerols shielding rough membranes to mediate hemocompatibility

Hemocompatible materials are needed for internal and extracorporeal biomedical applications, which should be realizable by reducing protein and thrombocyte adhesion to such materials. Polyethers have been demonstrated to be highly efficient in this respect on smooth surfaces. Here, we investigate the grafting of oligo- and polyglycerols to rough poly(ether imide) membranes as a polymer relevant to biomedical applications and show the reduction of protein and thrombocyte adhesion as well as thrombocyte activation. It could be demonstrated that, by performing surface grafting with oligo- and polyglycerols of relatively high polydispersity (>1.5) and several reactive groups for surface anchoring, full surface shielding can be reached, which leads to reduced protein adsorption of albumin and fibrinogen. In addition, adherent thrombocytes were not activated. This could be clearly shown by immunostaining adherent proteins and analyzing the thrombocyte covered area. The presented work provides an important strategy for the development of application relevant hemocompatible 3D structured materials

Trend in Obesity Prevalence in European Adult Cohort Populations during Follow-up since 1996 and Their Predictions to 2015

To investigate trends in obesity prevalence in recent years and to predict the obesity prevalence in 2015 in European populations.Data of 97,942 participants from seven cohorts involved in the European Prospective Investigation into Cancer and Nutrition (EPIC) study participating in the Diogenes project (named as "Diogenes cohort" in the following) with weight measurements at baseline and follow-up were used to predict future obesity prevalence with logistic linear and non-linear (leveling off) regression models. In addition, linear and leveling off models were fitted to the EPIC-Potsdam dataset with five weight measures during the observation period to find out which of these two models might provide the more realistic prediction.During a mean follow-up period of 6 years, the obesity prevalence in the Diogenes cohort increased from 13% to 17%. The linear prediction model predicted an overall obesity prevalence of about 30% in 2015, whereas the leveling off model predicted a prevalence of about 20%. In the EPIC-Potsdam cohort, the shape of obesity trend favors a leveling off model among men (R²  = 0.98), and a linear model among women (R² = 0.99).Our data show an increase in obesity prevalence since the 1990ies, and predictions by 2015 suggests a sizeable further increase in European populations. However, the estimates from the leveling off model were considerably lower

Consumption of predefined 'Nordic' dietary items in ten European countries - an investigation in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort.

Health-beneficial effects of adhering to a healthy Nordic diet index have been suggested. However, it has not been examined to what extent the included dietary components are exclusively related to the Nordic countries or if they are part of other European diets as well, suggesting a broader preventive potential. The present study describes the intake of seven a priori defined healthy food items (apples/pears, berries, cabbages, dark bread, shellfish, fish and root vegetables) across ten countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) and examines their consumption across Europe

Association of Sleep Duration with Chronic Diseases in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study

Background: In view of the reduced number of hours devoted to sleep in modern western societies the question arises what effects might result from sleep duration on occurrence of chronic diseases. Methods: Data from 23 620 middle-aged participants of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study, that were recruited between 1994–1998, were analyzed by using Cox proportional hazard regression to examine the association between self-reported sleep duration at baseline and incidence of chronic diseases, such as diabetes, myocardial infarction, stroke, and cancer. Results: During a mean follow-up period of 7.8 years 841 incident cases of type 2 diabetes, 197 cases of myocardial infarction, 169 incident strokes, and 846 tumor cases were observed. Compared to persons sleeping 7-,8 h/day, participants with sleep duration of,6 h had a significantly increased risk of stroke (Hazard Ratio (HR) = 2.06, 95

Hazard ratios (HR) and 95% Confidence Intervals for Type 2 Diabetes, Myocardial Infarction, Stroke, and Cancer by Sleep Duration in the EPIC-Potsdam Cohort.

a<p>Type 2 diabetes, myocardial infarction, stroke, or cancer, whichever occurs first.</p>b<p>Stratified by age and adjusted for sex.</p>c<p>Additionally adjusted for sleeping disorders (yes/no), alcohol intake from beverages (non-consumers, men: >0–12 g/d, >12–24 g/d, >24 g/d; women: >0–6 g/d, >6–12 g/d, >12 g/d), smoking status (never, former, current), walking, cycling, sports (hours/week), employment status (employed vs. unemployed), and education (technical school or lower degree vs. university of applied sciences or university degree).</p>d<p>Adjusted for the variables in model 2 plus potential mediators: BMI (kg/m²), waist-to-hip ratio, prevalent hypertension at baseline (yes/no), and history of high blood lipid levels at baseline (yes/no).</p>e<p>Adjusted for the variables in model 3 plus consumption of caffeinated beverages (coffee and tea in g/day), satisfaction with life (4 levels: very satisfied, rather satisfied, rather dissatisfied, very dissatisfied), satisfaction with health (4 levels: very satisfied, rather satisfied, rather dissatisfied, very dissatisfied), and intake of antidepressants (yes/no).</p>f<p>Model 3–4 for cancer includes the same covariable-set as models for type 2 diabetes, myocardial infarction, and stroke, except prevalent hypertension at baseline (yes/no), and history of high blood lipid levels at baseline (yes/no).</p

Association of sleep duration with overall chronic disease risk.

<p>Stratified by age and adjusted for sex, sleeping disorders, alcohol intake from beverages, smoking status, walking/cycling/sports, employment status, education, BMI, waist-to-hip ratio, prevalent hypertension at baseline, history of high blood lipid levels at baseline, consumption of caffeinated beverages, satisfaction with life, satisfaction with health, and intake of antidepressants.</p

Hazard ratios (HR) and 95% confidence intervals for daytime sleep and chronic diseases stratified by prevalent hypertension.

a<p>Type 2 diabetes, myocardial infarction, stroke or cancer, whichever occurs first.</p>b<p>Stratified by age and adjusted for sex.</p>c<p>Additionally adjusted for sleeping disorders (yes/no), sleep duration at night (<6, 6-<7, 7-<8, 8-<9, ≥9 h), alcohol intake from beverages (non-consumers, men: >0–12 g/d, >12–24 g/d, >24 g/d; women: >0–6 g/d, >6–12 g/d, >12 g/d), smoking status (never, former, current), walking, cycling, sports (hours/week), employment status (employed vs. unemployed), and education (technical school or lower degree vs. university of applied sciences or university degree).</p>d<p>Adjusted for the variables in model 2 plus BMI (kg/m2), waist-to-hip ratio, and history of high blood lipid levels at baseline (yes/no).</p>e<p>Model 3 for cancer includes the same covariates as models for type 2 diabetes, myocardial infarction and stroke, except history of high blood lipid levels at baseline (yes/no).</p

Baseline Characteristics and Risk Factors for Chronic Disease by Self-reported Sleep Duration.

<p>Categorical variables are presented as percentages within the respective subgroup and continuous variables are expressed as means with standard deviation in parentheses.</p>a<p>Low-energy beverages: water, coffee (including de-caffeinated coffee), tea (including herbal tea), low-energy soft drinks (energy-reduced cola, lemonades).</p>b<p>High-energy beverages: juice, high-energy soft drinks (cola, lemonades, non-alcoholic beer, malt beer).</p>c<p>Caffeinated beverages: coffee, black tea.</p>d<p>Vegetables are also including legumes.</p>e<p>Side dishes: pasta, rice, potatoes.</p>f<p>Dairy products: milk, yoghurt, curd, soured milk/kefir, cream, cheese.</p>g<p>Meat: red meat, poultry, processed meat.</p>h<p>Snacks: french fries, pizza, chips.</p>i<p>Sweets: cakes, cookies, confectionary, sweet bread spread, desserts.</p