Being fit and healthy even at high age is a high value for many people. A key factor in this is a healthy diet. People, who eat well and healthily throughout their lives, are less likely to be ill in old age. But how exactly is healthy nutrition defined? How healthy do older people in Germany eat? Would they even accept new nutrition recommendations? The competence cluster Nutritional Intervention for Healthy Aging (NutriAct) is investigating these questions in the Berlin-Potsdam area. The aim is to create the scientific basis for age-appropriate nutritional interventions and recommendations. In addition, new foods are to be developed that enable healthy nutrition. The cluster focuses in particular on the middle-aged population (50-70 years) in order to promote healthy aging beyond the age of 70. To this end, the neurobiological, psychological, social and familial basis of food choices and, in particular, their influencing factors within family structures, will be analyzed. In addition, research will be conducted to determine whether sociocultural factors influence taste preferences and dietary practices. A central question here is the significance of gender-specific effects in connection with taste orientations within a partnership. In addition, the extent to which moments of upheaval, such as the transition to retirement, separation, or an illness, influence eating practices will be examined. On the basis of the nutrition pattern analyses, realistically implementable nutrition recommendations will be developed. The central element of the cluster is an intervention study in which the effectiveness, adherence and acceptance of the NutriAct dietary pattern rich in plant proteins, fiber and unsaturated fatty acids will be systematically tested. Based on this, new healthy and palatable foods will be developed that will be accepted by consumers. Accompanying this will be the identification of new biomarkers that predict the effects of a particular diet on health status, especially in old age.
The HiGHmed consortium aims to develop and use innovative information infrastructures to increase the efficiency of clinical research and to swiftly translate research results into validated improvements of patient care. These aims are tightly connected with challenges to integrate and further develop solutions of innovative, internationally interoperable data integration and methods, with the aim to demonstrate their added value for health research and patient care. The concepts must be designed in a way that will help to develop sustainable structures and with the perspective for an easy roll-out to other hospitals. You might want to refer to the HiGHmed consoritum website for further details.
Heidelberg University Hospital (UKL-HD)
University Medical Center Göttingen (UMG)
Hannover Medical School (MHH)
University Hospital Schleswig-Holstein / Kiel University (UKSH)
The cooperation project Systems Medicine of Heart Failure (SMART) focuses on researching risk factors of heart failures. The onset and course of heart failure (HF) is triggered by a complex regulatory network that includes stressors (pressure overload by individual anatomic hemodynamic settings), intrinsic (genes), environmental (regulating epigenetics), and modifying factors (such as hormones and the immune system). SMART aims to establish individualized strategies for the prevention and management of HF by early detection of the physiological, genomic, proteomic and hemodynamic mechanisms that lead from one common cause of ventricular dysfunction (pressure overload) to maladaptive remodeling and irreversible HF. To cope with the complexity of HF, SMART will interrelate models describing the interplay between genome, proteome and cell function, regulating hormones, tissue composition and hemodynamic whole organ function up to a whole body description of a patient and patient cohorts. The ultimate goal is to demonstrate proof-of-concept tools for predicting disease evolution and efficacy of treatment in a given patient. To achieve this task SMART will apply – A modelling framework that couples multi-scale mechanistic models with in-depth genome/proteome, cell physiology and whole organ (biomechanical and fluid dynamic) models – Subsequently, investigate methods validity and relevance for “quantitative prediction” of treatment outcome in a clinical proof-of-concept trial (demonstrator) of patients with aortic valve diseases.