Patricia Matus ¹ Alejandro Sepúlveda-Peñaloza ¹ Keneth Page ² Claudia Rodriguez ¹ Marcela Cárcamo ¹ Francisco Bustamante ¹ Marcela Garrido ¹ Cinthya Urquidi ^1*

• ¹ Facultad de Medicina, Universidad de los Andes, Chile, Santiago, Chile
• ² Facultdad de Medicina, Universidad de los Andes, Chile, Santiago, Santiago Metropolitan Region (RM), Chile

The double burden of diseases and scarce resources in developing countries highlight the need to change the conceptualization of health problems and translational research.Contrary to the traditional paradigm focused on genetics, the exposome paradigm proposed in 2005 that complements the genome is an innovative theory. It involves a holistic approach to understanding the complexity of the interactions between the human being's environment throughout their life and health. This paper outlines a scalable framework for exposome research, integrating diverse data sources for comprehensive public health surveillance and policy support. The Chilean exposome-based system for ecosystems (CHiESS) project proposes a conceptual model based on the ecological and One Health approaches, and the development of a technological dynamic platform for exposome research, which leverages available administrative data routinely collected by national agencies, in clinical records, and by biobanks. CHiESS considers a multilevel exposure for exposome operationalization, including the ecosystem, community, population, and individual levels. CHiESS will include four consecutive stages for development into an informatic platform: 1) environmental data integration and harmonization system, 2) clinical and omics data integration, 3) advanced analytical algorithm development, and 4) visualization interface development and targeted population-based cohort recruitment. The ChiESS platform aims to integrate and harmonize available secondary administrative data and provide a complete geospatial mapping of the external exposome. Additionally, it aims to analyze complex interactions between environmental stressors of the ecosystem and molecular processes of the human being and their effect on human health. Moreover, by identifying exposome-based hotspots, CHiESS allows the targeted and efficient recruitment of population-based cohorts for translational research and impact evaluation. Utilizing advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and blockchain, this framework enhances data security, real-time monitoring, and predictive analytics. The CHiESS model is adaptable for international use, promoting global health collaboration and supporting sustainable development goals.