In 2013, the American Medical Association (AMA) decided to recognize obesity as a disease. One of the main arguments presented in favor of this was broadly 'utilitarian': the disease label would, it was claimed, provide more benefits than harms and thereby serve the general good. Several individuals and groups have argued that this reasoning is just as powerful in the European context. Drawing mainly on a review of relevant social science research, we discuss the validity of this argument. Our conclusion is that in a Western European welfare state, defining obesity as a disease will not on balance serve the general good, and that it is therefore more appropriate to continue to treat obesity as a risk factor. The main reasons presented in favor of this conclusion are: It is debatable whether a disease label would lead to better access to care and preventive measures and provide better legal protection in Europe. Medicalization and overtreatment are possible negative effects of a disease label. There is no evidence to support the claim that declaring obesity a disease would reduce discrimination or stigmatization. In fact, the contrary is more likely, since a disease label would categorically define the obese body as deviant.

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President Trump has issued executive orders transforming US immigration policy, potentially harming patient health and well-being. Are the president's orders lawful and ethical, and what are the effects on the health system? Border Wall

Despite the ever-progressing technological advances in producing data in health and clinical research, the generation of new knowledge for medical benefits through advanced analytics still lags behind its full potential. Reasons for this obstacle are the inherent heterogeneity of data sources and the lack of broadly accepted standards. Further hurdles are associated with legal and ethical issues surrounding the use of personal/patient data across disciplines and borders. Consequently, there is a need for broadly applicable standards compliant with legal and ethical regulations that allow interpretation of heterogeneous health data through in silico methodologies to advance personalized medicine. To tackle these standardization challenges, the Horizon2020 Coordinating and Support Action EU-STANDS4PM initiated an EU-wide mapping process to evaluate strategies for data integration and data-driven in silico modelling approaches to develop standards, recommendations and guidelines for personalized medicine. A first step towards this goal is a broad stakeholder consultation process initiated by an EU-STANDS4PM workshop at the annual COMBINE meeting (COMBINE 2019 workshop report in same issue). This forum analysed the status quo of data and model standards and reflected on possibilities as well as challenges for cross-domain data integration to facilitate in silico modelling approaches for personalized medicine.