Description du projet
Exposer l’impact des facteurs environnementaux sur les maladies pulmonaires invalidantes
Le concept d’exposome reflète la mesure cumulative des influences environnementales et des réponses biologiques associées à partir de la période périnatale, y compris les expositions à l’environnement, à l’alimentation, au comportement et aux processus endogènes. Son importance dans la santé et les maladies est indéniable, en particulier pour les maladies respiratoires chroniques, car elles sont les principales causes de décès liés à l’environnement. La bronchopneumopathie chronique obstructive et la mucoviscidose sont deux maladies respiratoires chroniques très invalidantes partageant des caractéristiques communes, mais présentant des racines opposées: la première semble être étroitement liée à l’exposome tandis que la seconde ne l’est pas. Pour faire face à l’impact de l’exposome sur l’évolution de ces deux maladies, le projet REMEDIA financé par l’UE développe des approches combinant la collecte d’exposomes et de données cliniques, l’apprentissage automatique avancé, l’utilisation de chambres de simulation atmosphérique et le développement de capteurs individuels. Une telle approche pourrait ouvrir la voie à une prévention et un traitement efficaces.
Objectif
Chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF) are two very debilitating non-communicable diseases that are of particular interest to consider in parallel in a human exposome study. Their roots are opposite: COPD is currently considered to be mainly related to the external exposome, while factors outside of the exposome play a major role in CF. However, COPD and CF share common characteristics such as high phenotypic variability of unknown origin, which prevents good therapeutic efficacy. It is therefore clear that the overall picture must be supplemented by taking into account additional components of the exposome than those currently considered in COPD and CF. Thus, the overall objective of the REMEDIA project is to extend the understanding of the contribution of the exposome, taken as a complex set of different components, to COPD and CF diseases. We will exploit data from existing cohorts and population registries in order to create a unified global database gathering phenotype and exposome information; we will develop a flexible individual sensor device combining environmental and biomarker toolkits; and use a versatile atmospheric simulation chamber to simulate the health effects of complex exposomes. We will use machine learning supervised analyses and causal inference models to identify relevant risk factors; and econometric and cost-effectiveness models to assess the costs, performance and cost-effectiveness of a selection of prevention strategies. The results will be used to develop guidelines to better predict disease risks and constitute the elements of the REMEDIA toolbox (global unified database, sensor device, versatile atmospheric simulation chamber, machine learning supervised analyses, causal inference model, Pan-European multi-criteria risk assessment tool, econometric models, cost-effectiveness models, new guidelines and recommendations). Deciphering the impact of environmental components throughout life on the phenotypic variability of COPD and CF could represent a major breakthrough in reducing morbidity and mortality associated with these two non-curable diseases and would lead to the identification of modifiable risk factors on which preventive action could be implemented. REMEDIA will be part of the European Human Exposome Network established between the 9 projects funded within the Human Exposome programme call H2020-SC1-BHC-2018-2020.
Champ scientifique
- social scienceseconomics and businesseconomicseconometrics
- social sciencessociologydemographymortality
- natural sciencescomputer and information sciencesdatabases
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Régime de financement
RIA - Research and Innovation actionCoordinateur
75654 Paris
France