MOSAICProject reference: 600914
Funded under :
MOSAIC - MOdels and Simulation techniques for discovering diAbetes Influence faCtors
MOSAIC will address two very specific aspects linked to the prediction of risk of developing diabetes (type 2 and gestational) and complications associated to diabetes. These objectives respond to a widely recognized problem related to diabetes management and have the potential to have a major impact in the way diabetes is currently diagnosed and followed in Europe.\nThe MOSAIC consortium counts with the expertise of four modelling partners who have worked over 25 years in the development of models of the human metabolic response in diabetes that will be enhanced in the project with the incorporation of elements that provide information related to environmental and clinical factors that prove to be relevant for the objectives defined such as socio-economic aspects, geographic localization, cultural background, nutrition, etc.\n\n\nMultiple data bases cutting across geographic boundaries are available to the MOSAIC consortium as a result of the activities of previous studies and projects of the members, such as (a) METABO 7FP EU project; (b) from the transversal study "Healthy Breakfast" enriched with Medtronic's CareLink© reports for continuous glucose monitoring systems; (c) two large longitudinal epidemiological studies over 10 years long (VIVA study, BOTNIA prospective study); (d) outpatient data treated by FSM, Athens Hospital, Health Department 'Valencia-La Fe', ASL Pavia program over more than 10 years and (e) other data bases generated in ongoing 7FP EU studies like ePREDICE.\n\nMOSAIC will integrate these models into an already existing platform for diabetes management and remote monitoring, NOMHAD Chronic, to facilitate the interpretation and visualization of the data and to enable a comprehensive understanding of the information by the health care professionals. At the same time this platform will be used during the validation phase of the project to acquire data during the prospective study to feed the models under test.
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