The Chilean exposome-based system for ecosystems project: Protocol for the development of an informatics platform for national data integration

The Chilean exposome-based system for ecosystems project: Protocol for the development of an informatics platform for national data integration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Study protocol The Chilean exposome-based system for ecosystems project: Protocol for the development of an informatics platform for national data integration Patricia Matus, Alejandro Sepúlveda-Peñaloza, Keneth Page, Claudia Rodriguez, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3914159/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The double burden of diseases and scarce resources in developing countries highlight the need to change conceptualization of health problems and development of translational research. Contrary to the traditional paradigm focused on genetics, the exposome approach 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. Herein, we have describe the conceptual model and technological system development of the Chilean exposome-based system for ecosystems (CHiESS). It is an intelligent and dynamic system for human exposome research, which leverages available administrative data routinely collected by national agencies, in clinical records, and by biobanks. Based on the ecological theory and one-health ecosystem approach, 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 exosome-based hotspots, CHiESS allows the targeted and efficient recruitment of population-based cohorts for translational research and impact evaluation. Exposome Data Integration Ecosystems Systems of system Figures Figure 1 Figure 2 1. Introduction The persistently high burden of non-communicable diseases (NCDs) worldwide ( 1 , 2 ) highlights the shortcomings of preventive interventions in real-world settings. After being born, individuals live in a complex ecosystem where the environment influences their risk of developing diseases and the effectiveness of interventions evaluated in controlled experiments ( 3 , 4 ). A small proportion of diseases are genetic in origin, and the majority are attributable to environmental factors ( 5 – 7 ). Likewise, predictive models in precision medicine demonstrate a significant unexplained variability, which is attributed to contextual factors such as social determinants or environmental factors ( 8 , 9 ). Studies have often focused on individual factors or specific environmental exposures. In 2005, Wild proposed a novel exposome approach to complement the human genome in cancer research that considers the totality of exposures throughout a person’s life and their complex interactions, including chemical, biological, psychosocial, and lifestyle factors ( 10 ). Subsequently, Wild stated that the exposome approach comprises the external (general and specific) exposome, which considers external environmental exposures, and the internal exposome, which emphasizes the influence of intrinsic biological processes (the omics) and individual characteristics ( 11 ). Thus, exposome studies provide a more nuanced understanding of the complex relationship between environmental exposures and health, ultimately contributing to the development of more effective strategies for disease prevention and public health interventions. The one-health approach can complement exposome studies in which human life and other species share the same ecosystem. In such ecosystems, disturbances in one species can help to understand and promote health in the other species ( 12 , 13 ). Several exposome-based initiatives have emerged, such as the HELIX, HERCULES, EXPOsOMIC, EU-EPHOR, ATLETE, and EU EXPANSE projects ( 14 – 19 ). Most of these projects focus on European populations with varied geographical scopes, specific diseases, or health outcomes, such as early-life development, cardiovascular and metabolic diseases, chronic respiratory diseases, or major chronic diseases. The projects often involve an interdisciplinary collaboration to develop tools for primary exposome research by integrating data from various sources, primarily previously recruited birth cohorts. Although mega-cohort studies, including biobanks, provide the framework for exposomic research, they require substantial investment. Latin American (LATAM) and other developing countries have different socioeconomic contexts and scarce research resources. Additionally, assembling population cohorts is expensive. Conversely, administrative data that is routinely collected provide insights into societal trends, resource allocation, and program effectiveness, and they are valuable for policy and decision-making across various sectors. Moreover, the Pan American Health Organization coordinated public health efforts to strengthen epidemiological surveillance in LATAM countries, providing a platform for member countries to share epidemiological data and information. Most countries face a double burden of diseases, which refers to the coexistence of communicable (infectious) and noncommunicable (chronic) diseases within a population, community, or individual. This phenomenon is particularly relevant in transitional or developing societies that are undergoing rapid social, economic, and demographic changes ( 20 ). Chile, a LATAM country, has demonstrated a sustained rapid economic growth over the last few decades. However, a socioeconomic gap persist, with income inequality being a notable concern. The epidemiological transition in Chile reflects a shift from a predominance of infectious diseases to an increasing burden of NCD, which is often associated with lifestyle changes. The coexistence of communicable diseases and NCDs highlights the complex health landscape during Chile’s economic evolution ( 21 ). The purpose of this communication is to describe the conceptual model of the Chilean exposome-based system for ecosystems CHiESS project, which is complemented by the one-health approach, and the technological development of a platform that allows the integration of existing administrative data, operationalization of exposome, and evaluation of the exposome impact on health outcomes. 2. Protocol description 2.1 Aim of CHiESS The primary purpose of the CHiESS project is to develop an intelligent technological platform that allows exposome operationalization and to investigate its effects on human health using administrative data. Thus, we aim to better understand the etiology of the disease and the translation of scientific evidence of both individual and population interventions to real-world settings. The specific objectives of the CHiESS project are as follows: 1. To build a dynamic and automated system that allows the integration, analysis, and visualization of a wide range of environmental stressors and individual data. 2. To operationalize and map the external and internal exposomes for identifying and monitoring novel hotspots. 3. To study the exposome contribution to the disease’s etiology and the impact of preventive interventions. The protocol was approved by the Scientific Ethics Committee of the Universidad de los Andes, Chile. 2.2 Administrative data sources Leveraging available administrative data is a differentiating focus of CHiESS. Administrative data sources encompass a wide range of information that governmental and organizational entities collect for administrative purposes. These sources include the following: - Government agencies: National, regional, and local government agencies collect data for various functions, such as census and national surveys. - Healthcare systems: Public and private hospitals and health insurance providers maintain data on patient demographics, medical treatments, and outcomes. - Epidemiological surveillance systems: Health-related data is systematically collected to monitor the spread of diseases, identify its trends, detect outbreaks early, and inform the public regarding health interventions and decisions. - Educational institutions: Schools, colleges, and universities gather data on student enrollment, academic performance, and other educational metrics. - Social services: Agencies responsible for social welfare, housing, and employment collect data on individuals and families receiving assistance. - Employment and labor departments: These agencies collect data on employment, wages, and workforce demographics. - Transportation authorities: Agencies managing transportation infrastructure gather data on traffic patterns, public transit usage, and road conditions. - Environmental agencies: Organizations responsible for ecological regulation collect data on pollution levels (air, water, and soil), emissions, and environmental compliance. - Statistical agencies: National statistical offices compile and disseminate data for statistical purposes, including economic indicators and population demographics. 2.3 CHiESS research and conceptual framework The CHiESS project’s data integration is based on scientific hypotheses and agnostic approaches. The CHiESS project raises the following three general research questions, which address priority current diseases according to biological plausibility and the current state of knowledge: a) What environmental exposure patterns, temporal accumulation of patterns, interaction of patterns, or mechanisms contribute to the diseases? b) Can omics signals or biomarkers of the health effects of multiple and prolonged exposures to environmental stressors be identified? c) What individual- or population-based interventions could be targeted using the exposome paradigm? The agnostic approach identifies new disease etiologies using omics or non-omic technologies, without adhering to specific preconceptions or predetermined conclusions. Based on the ecological theory, which examines patterns and processes in nature at different levels of organization (from individual units to larger systems), and according to the administrative division of Chile, the CHiESS conceptual model consists of four levels of exposure: 1) ecosystem, 2) community, 3) population, and 4) individual (Figure 1). Ecosystem: Continental Chile is divided into 16 regions from north to south. Each region has a government for its administration, with different climates, soils, economic activities (e.g., mining and agriculture industry), and biodiversity due to the large geographical length of the country. For example, the northern regions are characterized by deserts and arid areas, as well as the high peaks of the Andes mountains. In the southern regions, the ecosystem ranges from humid jungles to glacial areas. Moreover, urbanization and intensive agricultural practices prevail in the central regions due to its less extreme climate. Thus, because an ecosystem includes the biotic organisms and abiotic factors of a given area, the most macro or first level of exposure in CHiESS is the ecosystem of each region, including the political, economic, and climate exposures (Figure 1). Community: The regions are geographically subdivided into 346 communes, which are distributed unevenly among the 16 regions. The commune is the smallest unit of the local administrative government, and the municipality manages its local affairs. According to the ecological theory, a community includes all populations of different species that coexist in each location, including mammals, birds, and other species necessary to spread diseases or be the etiological cause in humans. Thus, each commune represents the community level or second level at CHiESS and includes the behavior of species other than humans (zoonotic vector and domestic and productive animals) and the social, educational, structural or chemical exposures in the area (Figure 1). Population: The ecological organization theory examines all individuals of the same species in a specific area at a given time. Because the population density for each commune differs, they have diverse ecosystems. CHiESS focuses on human health improvement. This third level includes other exposures or behaviors of the human species at a population level, including the level of exposure to environmental contaminants, lifestyles, and psychosocial-, economic-, or health-aggregated indicators (Figure 1). Figure 1 . Conceptual model Individual: At this level of organization, a single organism and its physiology, behavior, and adaptations are included. Therefore, at the individual level the same population exposures will be evaluated as that at the population level, but at the personal level. Additionally, the internal exposome represented by its omics will be studied (Figure 1). CHiESS outcomes: The project aims to provide evidence for both local public policies and precision medicine for NCDs, re-emerging and emerging infectious diseases, and countries facing the double burden of disease. The CHiESS project includes outcomes for NCD and communicable diseases in relation to both their etiology and spread. The health effects will also be evaluated at multiple levels. The outcomes are defined at the individual and population levels (green arrows in Figure 1). The symbol in Figure 1 denotes the complex interaction between the multilevel exposures within each level and between levels. The symbol corresponds to the recursiveness of health effects, (i.e., how individual outcomes contribute to population indicators and vice versa). Finally, CHiESS considers the cumulative and dynamic nature of exposures by studying how exposures accumulate and interact across different life stages of the individual, from prenatal development through childhood, adolescence, adulthood, and into old age. The cumulative exposition for the population level and the macros geospatial levels represent the ecosystem and the community, respectively. CHiESS technological framework The leading architecture and data interactions in CHiESS for the platform are based on 5 layers that evolve in a dynamic and robust environment: 1) diverse data sources, 2) data wrangling, 3) data warehousing, 4) discover engine, and 5) data dashboard (Figure 2). Figure 2 . Technological framework of the platform The CHIESS platform architecture seamlessly integrates external exposome information with individual omics and healthcare provider data. This first layer manages a diverse array of external data sources from government agencies, epidemiological surveillance systems, educational institutions, social services, employment and labor departments, transportation authorities, environmental agencies, and statistical agencies. The data may include relevant information such as air quality indices, pollution levels, socioeconomic indicators, lifestyle information, and zoonotic vectors. This layer also interfaces seamlessly with individual omics data from healthcare systems, clinical data, electronic health records, lifestyle assessments, and other original studies. Additionally, it incorporates genomics, epigenomics, transcriptomics, metabolomics, and proteomics to provide a complete view of an individual’s biological makeup. Collection of this data will be challenging because the information should be reconciled cohesively. To overcome these challenges, the second layer addresses the data heterogeneity with a robust data wrangling and preparation process that harmonizes the diverse datasets. This ensures consistency and quality of the integrated datasets, addresses potential discrepancies, and enhances the overall reliability of the exposome studies. The architecture employs sophisticated data integration and transformation techniques, ensuring that disparate data types are harmonized and normalized to facilitate meaningful correlations and analyses across dimensions. This transformation process involves careful consideration of temporal spatial dimensions, standardization, and normalization, as well as data quality checks to eliminate potential biases. A secure cloud-based data warehousing layer efficiently stores the pre-processed data to ensure an efficient and protected environment for sensitive information. This layer serves as a dynamic interface that facilitates efficient storage and allows scalable and flexible data management. Researchers, stakeholders, and policymakers benefit from this central repository that streamlines collaborative efforts and ensures data integrity. In the final dual-layer, an engine for advanced algorithms operates in tandem with a visualization tool to provide a comprehensive platform for analysis and interpretation. This final engine layer extracts patterns and associations from the integrated data via two types of analyses (hypothesis-based and agnostic algorithms). Simultaneously, the visualization tool translates complex findings into interpretable insights, which facilitates comprehension and decision-making. The platform capitalizes on advanced techniques to transform complex data into interpretable insights. A diverse array of visualization methods is employed to present findings in a meaningful, accessible, and effective manner. Graphical representations, such as interactive charts, heatmaps, and network diagrams, offer intuitive insights into patterns and relationships within the integrated datasets. Geographic visualizations overlaying the exposome data on maps facilitate the identification of spatial trends, which is crucial for understanding regional variations in environmental exposures and health outcomes. Furthermore, it incorporates dynamic views that enable historical and real-time exploration of data trends. Thus, stakeholders can focus on specific variables or timeframes which facilitate targeted analyses. This interactive approach enhances user experience and promotes collaboration by allowing diverse stakeholders to contribute their expertise to the exposome studies. 3. Project stages development CHIESS will have four consecutive stages for the development into an informatic platform. 3.1 Stage one: Environmental data integration and harmonization system The first stage aims to develop the initial data integration and harmonization components. This stage also involves the development of a data integration application and the processing of queries to allow geospatial operationalization of the general external exposome at the ecosystem (regions) and community (commune) levels. Validation at this stage will consist of determining data inconsistency, integrity, and posttransformation accuracy according to data governance policies and privacy regulations. 3.2 Stage two: Health and omics data integration The second stage focuses on developing complementary informatics components for integrating and harmonizing data from clinical records and biobanks. This will operationalize the specific external exposome at the population level and the internal exposome at the individual level. Validation at this stage will consist of determining data integrity and post-transformation accuracy in addition to confirming the concordance of systems results with well-known geospatial exposure distribution and omics-biomarkers outcomes. 3.3 Stage three: Development of advanced analytical algorithms The third stage involves developing hypothesis-based or supervised and unsupervised agnostic algorithms which complement causal diagrams. CHiESS is focused on geospatial complex interactions and the trend of multiple expositions analyses. Thus, the Fay-Herriot and Spatial Fay-Herriot models ( 22 ), Bayesian structural time series, Bayesian multiple index model, and mixed models with weighted cumulative exposure index will be considered. 3.4 Stage four: Visualization interface development and population-based cohort recruitment The fourth stage is focused on developing and implementing visualization and monitoring interfaces for mapping and surveillance hotspots for use by final users and stakeholders. Moreover, the identification of hotspots at this stage will help determine policy recommendations, plan interventions, and recruit a hotspot-targeted population-based cohort for the measurement of environmental stressors and omics as well as a final external validation. 4. Main challenges The challenges for operationalizing human exposome in practice have been previously identified; the totality of environmental exposures is tremendously challenging ( 23 – 27 ). The challenges include high dimension integration and mining data with time and spatial dependence structure, dealing with heterogeneity data, and development of wearable measurement sensors. Fortunately, with the advancements in bioinformatics, big data analytics, and artificial intelligence, these challenges can be overcome. The main challenges for CHiESS are incorporating causal structures in the integrated data, ensuring advanced analytics are appropriately tailored to a robust causal design that tests hypotheses, and understanding the role of confounders. Therefore, epidemiological and clinical reasoning is essential during data integration and analysis. Since CHiESS is initially based on administrative data integration, validating exposure and outcome data measurements is another challenge. The application of known exposure or outcomes biomarkers, complemented by validated questionnaire-based approaches, may overcome this limitation because altered mRNA, proteins, or metabolite levels will reflect specific environmental exposures. Notwithstanding the aforementioned limitations, matching omics measurements with functionalities (e.g., biomarker development, differentiating exposures from biological responses, investigating mixtures and interactions between agents, and understanding mechanisms for biological plausibility and etiology of diseases) is undoubtedly the major challenge for CHiESS due to its cost. Hence, the prospective cohort study design best suits the exposure biomarker approach, providing opportunities for repeat sampling to enable a broader timeframe of exposure assessment and avoid reverse causation by collecting samples before the disease onset. Thus, CHiESS provides a criteria for defining environmental health priorities, which will guide priority cohort recruitment as well as researching and tailoring omics measurement and target intervention at the population level. Because CHIESS is based on a one-health ecosystem approach, another challenge is understanding the particularities of each health problem and establishing connections with general processes such as globalization, territorial expansion, migration, job insecurity, vulnerability of populations, environmental degradation, and urbanization ( 28 ). Finally, ethical challenges in data integration could arise from the potential mishandling of data, privacy concerns, security risks, data governance, transparency and accountability, legal and regulatory compliance, and the impact on individuals and society. Thus, applying technical measures, legal frameworks, and ethical guidelines will ensure that data integration is conducted responsibly and transparently. 5. Discussion The complexity of human exposome requires to be investigated progressively. To our knowledge, CHiESS is an innovative exposome-based project in LATAM that contextualizes the human exposome and introduces the exposome paradigm in translational research. Unlike other initiatives, the CHiESS model is based on the one-health ecosystem approach; the patterns of other species could contribute to the spread of communicable diseases. Furthermore, CHiESS focuses on the geospatial operationalization of the general external exposome based on the administrative and routinely collected data. The novel CHiESS technological platform involves developing automated and semi-automated systems for data integration, advanced analytics and visualization, and monitoring, which is similar to other platforms ( 29 , 30 ). This will facilitate meaningful exposome research and empower researchers and stakeholders to derive significant conclusions from the integrated data and advanced analytics. Nevertheless, it is essential to carry out a proof of concept study to validate the feasibility and effectiveness of data integration and harmonization before it can be fully developed. In conclusion, the CHiESS is a novel project that will provide an informatics and intelligent platform that will integrate administrative and original target omics data for exposome studies in Latin America. Most Latin American and developing countries are facing the double burden of diseases. Thus, the CHiESS conceptual model is based on the one-health ecosystem approach. It includes environmental stressors and other species’ patterns that contribute to the transmission of infectious diseases and the etiology of non-communicable diseases. CHiESS will serve as a basis for targeted, evidence-based interventions, efficient and tailored population-based cohort recruitment, and omics measurements. Declarations Ethics Approval The protocol was approved by the Scientific Ethics Committee of the Universidad de los Andes, Chile. Folio number 2024005. Conflict of Interest The Authors declare that there is no conflict of interest. Source of Funding This work received no funding. Author contribution PM, AS, and CU, the study. KP and AS contributed with the technology design CR, MC, FB and MG participated in the manuscript written. CU and PM wrote and edited the manuscripts. References Institute for Health Metrics and Evaluation (IHME). GBD Compare Data Visualization. Seattle, WA: IHME, University of Washington., 2020. Available from http://vizhub.healthdata.org/gbd-compare . (Accessed [September, 2023]). Ong KL, Stafford LK, McLaughlin SA, Boyko EJ, Vollset SE, Smith AE, et al. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402(10397):203–34. 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17:27:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":710694,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConceptual model\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3914159/v1/0c14298420adff912405368e.jpg"},{"id":50751144,"identity":"61be2392-38ce-44f7-ab17-1800b18b9bff","added_by":"auto","created_at":"2024-02-06 17:35:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTechnological framework of the platform\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3914159/v1/27e55195cb3d0e88d7c677a1.jpg"},{"id":53473799,"identity":"5667a301-d40d-4b4e-b56f-a3c8b475c898","added_by":"auto","created_at":"2024-03-26 12:09:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":453517,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3914159/v1/20cc2286-23a5-4d67-9321-8e7405fbcf23.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Chilean exposome-based system for ecosystems project: Protocol for the development of an informatics platform for national data integration","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe persistently high burden of non-communicable diseases (NCDs) worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) highlights the shortcomings of preventive interventions in real-world settings. After being born, individuals live in a complex ecosystem where the environment influences their risk of developing diseases and the effectiveness of interventions evaluated in controlled experiments (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). A small proportion of diseases are genetic in origin, and the majority are attributable to environmental factors (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Likewise, predictive models in precision medicine demonstrate a significant unexplained variability, which is attributed to contextual factors such as social determinants or environmental factors (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies have often focused on individual factors or specific environmental exposures. In 2005, Wild proposed a novel exposome approach to complement the human genome in cancer research that considers the totality of exposures throughout a person\u0026rsquo;s life and their complex interactions, including chemical, biological, psychosocial, and lifestyle factors (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Subsequently, Wild stated that the exposome approach comprises the external (general and specific) exposome, which considers external environmental exposures, and the internal exposome, which emphasizes the influence of intrinsic biological processes (the omics) and individual characteristics (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Thus, exposome studies provide a more nuanced understanding of the complex relationship between environmental exposures and health, ultimately contributing to the development of more effective strategies for disease prevention and public health interventions. The one-health approach can complement exposome studies in which human life and other species share the same ecosystem. In such ecosystems, disturbances in one species can help to understand and promote health in the other species (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral exposome-based initiatives have emerged, such as the HELIX, HERCULES, EXPOsOMIC, EU-EPHOR, ATLETE, and EU EXPANSE projects (\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Most of these projects focus on European populations with varied geographical scopes, specific diseases, or health outcomes, such as early-life development, cardiovascular and metabolic diseases, chronic respiratory diseases, or major chronic diseases. The projects often involve an interdisciplinary collaboration to develop tools for primary exposome research by integrating data from various sources, primarily previously recruited birth cohorts. Although mega-cohort studies, including biobanks, provide the framework for exposomic research, they require substantial investment. Latin American (LATAM) and other developing countries have different socioeconomic contexts and scarce research resources. Additionally, assembling population cohorts is expensive. Conversely, administrative data that is routinely collected provide insights into societal trends, resource allocation, and program effectiveness, and they are valuable for policy and decision-making across various sectors. Moreover, the Pan American Health Organization coordinated public health efforts to strengthen epidemiological surveillance in LATAM countries, providing a platform for member countries to share epidemiological data and information.\u003c/p\u003e \u003cp\u003eMost countries face a double burden of diseases, which refers to the coexistence of communicable (infectious) and noncommunicable (chronic) diseases within a population, community, or individual. This phenomenon is particularly relevant in transitional or developing societies that are undergoing rapid social, economic, and demographic changes (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Chile, a LATAM country, has demonstrated a sustained rapid economic growth over the last few decades. However, a socioeconomic gap persist, with income inequality being a notable concern. The epidemiological transition in Chile reflects a shift from a predominance of infectious diseases to an increasing burden of NCD, which is often associated with lifestyle changes. The coexistence of communicable diseases and NCDs highlights the complex health landscape during Chile\u0026rsquo;s economic evolution (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe purpose of this communication is to describe the conceptual model of the Chilean exposome-based system for ecosystems CHiESS project, which is complemented by the one-health approach, and the technological development of a platform that allows the integration of existing administrative data, operationalization of exposome, and evaluation of the exposome impact on health outcomes.\u003c/p\u003e"},{"header":"2. Protocol description","content":"\u003cp\u003e\u003cem\u003e2.1 Aim of CHiESS\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe primary purpose of the CHiESS project is to develop an intelligent technological platform that allows exposome operationalization and to investigate its effects on human health using administrative data. Thus, we aim to better understand the etiology of the disease and the translation of scientific evidence of both individual and population interventions to real-world settings.\u003c/p\u003e\n\u003cp\u003eThe specific objectives of the CHiESS project are as follows:\u003c/p\u003e\n\u003cp\u003e1. To build a dynamic and automated system that allows the integration, analysis, and visualization of a wide range of environmental stressors and individual data.\u003c/p\u003e\n\u003cp\u003e2. To operationalize and map the external and internal exposomes for identifying and monitoring novel hotspots.\u003c/p\u003e\n\u003cp\u003e3. To study the exposome contribution to the disease\u0026rsquo;s etiology and the impact of preventive interventions.\u003c/p\u003e\n\u003cp\u003eThe protocol was approved by the Scientific Ethics Committee of the Universidad de los Andes, Chile.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2 Administrative data sources\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLeveraging available administrative data is a differentiating focus of CHiESS. Administrative data sources encompass a wide range of information that governmental and organizational entities collect for administrative purposes. These sources include the following:\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp; \u0026nbsp; \u0026nbsp;Government agencies: National, regional, and local government agencies collect data for various functions, such as census and national surveys.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp; \u0026nbsp; \u0026nbsp;Healthcare systems: Public and private hospitals and health insurance providers maintain data on patient demographics, medical treatments, and outcomes.\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp; \u0026nbsp; \u0026nbsp;Epidemiological surveillance systems: Health-related data is systematically collected to monitor the spread of diseases, identify its trends, detect outbreaks early, and inform the public regarding health interventions and decisions.\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp; \u0026nbsp; \u0026nbsp;Educational institutions: Schools, colleges, and universities gather data on student enrollment, academic performance, and other educational metrics.\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp; \u0026nbsp; \u0026nbsp;Social services: Agencies responsible for social welfare, housing, and employment collect data on individuals and families receiving assistance.\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp; \u0026nbsp; \u0026nbsp;Employment and labor departments: These agencies collect data on employment, wages, and workforce demographics.\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp; \u0026nbsp; \u0026nbsp;Transportation authorities: Agencies managing transportation infrastructure gather data on traffic patterns, public transit usage, and road conditions.\u003c/p\u003e\n\u003cp\u003e- \u0026nbsp; \u0026nbsp; Environmental agencies: Organizations responsible for ecological regulation collect data on pollution levels (air, water, and soil), emissions, and environmental compliance.\u003c/p\u003e\n\u003cp\u003e- \u0026nbsp; \u0026nbsp; Statistical agencies: National statistical offices compile and disseminate data for statistical purposes, including economic indicators and population demographics.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3 CHiESS research and conceptual framework\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe CHiESS project\u0026rsquo;s data integration is based on scientific hypotheses and agnostic approaches. The CHiESS project raises the following three general research questions, which address priority current diseases according to biological plausibility and the current state of knowledge:\u003c/p\u003e\n\u003cp\u003ea)\u0026nbsp; \u0026nbsp;What environmental exposure patterns, temporal accumulation of patterns, interaction of patterns, or mechanisms contribute to the diseases?\u003c/p\u003e\n\u003cp\u003eb)\u0026nbsp; \u0026nbsp;Can omics signals or biomarkers of the health effects of multiple and prolonged exposures to environmental stressors be identified?\u003c/p\u003e\n\u003cp\u003ec)\u0026nbsp; \u0026nbsp;What individual- or population-based interventions could be targeted using the exposome paradigm?\u003c/p\u003e\n\u003cp\u003eThe agnostic approach identifies new disease etiologies using omics or non-omic technologies, without adhering to specific preconceptions or predetermined conclusions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the ecological theory, which examines patterns and processes in nature at different levels of organization (from individual units to larger systems), and according to the administrative division of Chile, the CHiESS conceptual model consists of four levels of exposure: 1) ecosystem, 2) community, 3) population, and 4) individual (Figure 1).\u003c/p\u003e\n\u003cp\u003eEcosystem: Continental Chile is divided into 16 regions from north to south. Each region has a government for its administration, with different climates, soils, economic activities (e.g., mining and agriculture industry), and biodiversity due to the large geographical length of the country. For example, the northern regions are characterized by deserts and arid areas, as well as the high peaks of the Andes mountains. In the southern regions, the ecosystem ranges from humid jungles to glacial areas. Moreover, urbanization and intensive agricultural practices prevail in the central regions due to its less extreme climate. Thus, because an ecosystem includes the biotic organisms and abiotic factors of a given area, the most macro or first level of exposure in CHiESS is the ecosystem of each region, including the political, economic, and climate exposures (Figure 1).\u003c/p\u003e\n\u003cp\u003eCommunity: The regions are geographically subdivided into 346 communes, which are distributed unevenly among the 16 regions. The commune is the smallest unit of the local administrative government, and the municipality manages its local affairs. According to the ecological theory, a community includes all populations of different species that coexist in each location, including mammals, birds, and other species necessary to spread diseases or be the etiological cause in humans. Thus, each commune represents the community level or second level at CHiESS and includes the behavior of species other than humans (zoonotic vector and domestic and productive animals) and the social, educational, structural or chemical exposures in the area (Figure 1).\u003c/p\u003e\n\u003cp\u003ePopulation: The ecological organization theory examines all individuals of the same species in a specific area at a given time. Because the population density for each commune differs, they have diverse ecosystems. CHiESS focuses on human health improvement. This third level includes other exposures or behaviors of the human species at a population level, including the level of exposure to environmental contaminants, lifestyles, and psychosocial-, economic-, or health-aggregated indicators (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure 1\u003c/em\u003e. \u003cem\u003eConceptual model\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIndividual: At this level of organization, a single organism and its physiology, behavior, and adaptations are included. Therefore, at the individual level the same population exposures will be evaluated as that at the population level, but at the personal level. Additionally, the internal exposome represented by its omics will be studied (Figure 1).\u003c/p\u003e\n\u003cp\u003eCHiESS outcomes: The project aims to provide evidence for both local public policies and precision medicine for NCDs, re-emerging and emerging infectious diseases, and countries facing the double burden of disease. The CHiESS project includes outcomes for NCD and communicable diseases in relation to both their etiology and spread. The health effects will also be evaluated at multiple levels. The outcomes are defined at the individual and population levels (green arrows in Figure 1).\u003c/p\u003e\n\u003cp\u003eThe \u003cimg width=\"22\" 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\" alt=\"Flecha circular con relleno sólido\"\u003e\u0026nbsp;symbol in Figure 1 denotes the complex interaction between the multilevel exposures within each level and between levels. The \u003cimg 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\" style=\"width: 31px;\"\u003e\u0026nbsp;symbol corresponds to the recursiveness of health effects, (i.e., how individual outcomes contribute to population indicators and vice versa).\u003c/p\u003e\n\u003cp\u003eFinally, CHiESS considers the cumulative and dynamic nature of exposures by studying how exposures accumulate and interact across different life stages of the individual, from prenatal development through childhood, adolescence, adulthood, and into old age. The cumulative exposition for the population level and the macros geospatial levels represent the ecosystem and the community, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCHiESS technological framework\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe leading architecture and data interactions in CHiESS for the platform are based on 5 layers that evolve in a dynamic and robust environment: 1) diverse data sources, 2) data wrangling, 3) data warehousing, 4) discover engine, and 5) data dashboard (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure 2\u003c/em\u003e. \u003cem\u003eTechnological framework of the platform\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe CHIESS platform architecture seamlessly integrates external exposome information with individual omics and healthcare provider data. This first layer manages a diverse array of external data sources from government agencies, epidemiological surveillance systems, educational institutions, social services, employment and labor departments, transportation authorities, environmental agencies, and statistical agencies. The data may include relevant information such as air quality indices, pollution levels, socioeconomic indicators, lifestyle information, and zoonotic vectors. This layer also interfaces seamlessly with individual omics data from healthcare systems, clinical data, electronic health records, lifestyle assessments, and other original studies. Additionally, it incorporates genomics, epigenomics, transcriptomics, metabolomics, and proteomics to provide a complete view of an individual\u0026rsquo;s biological makeup. Collection of this data will be challenging because the information should be reconciled cohesively.\u003c/p\u003e\n\u003cp\u003eTo overcome these challenges, the second layer addresses the data heterogeneity with a robust data wrangling and preparation process that harmonizes the diverse datasets. This ensures consistency and quality of the integrated datasets, addresses potential discrepancies, and enhances the overall reliability of the exposome studies. The architecture employs sophisticated data integration and transformation techniques, ensuring that disparate data types are harmonized and normalized to facilitate meaningful correlations and analyses across dimensions. This transformation process involves careful consideration of temporal spatial dimensions, standardization, and normalization, as well as data quality checks to eliminate potential biases.\u003c/p\u003e\n\u003cp\u003eA secure cloud-based data warehousing layer efficiently stores the pre-processed data to ensure an efficient and protected environment for sensitive information. This layer serves as a dynamic interface that facilitates efficient storage and allows scalable and flexible data management. Researchers, stakeholders, and policymakers benefit from this central repository that streamlines collaborative efforts and ensures data integrity.\u003c/p\u003e\n\u003cp\u003eIn the final dual-layer, an engine for advanced algorithms operates in tandem with a visualization tool to provide a comprehensive platform for analysis and interpretation. This final engine layer extracts patterns and associations from the integrated data via two types of analyses (hypothesis-based and agnostic algorithms). Simultaneously, the visualization tool translates complex findings into interpretable insights, which facilitates comprehension and decision-making. The platform capitalizes on advanced techniques to transform complex data into interpretable insights. A diverse array of visualization methods is employed to present findings in a meaningful, accessible, and effective manner. Graphical representations, such as interactive charts, heatmaps, and network diagrams, offer intuitive insights into patterns and relationships within the integrated datasets. Geographic visualizations overlaying the exposome data on maps facilitate the identification of spatial trends, which is crucial for understanding regional variations in environmental exposures and health outcomes. Furthermore, it incorporates dynamic views that enable historical and real-time exploration of data trends. Thus, stakeholders can focus on specific variables or timeframes which facilitate targeted analyses. This interactive approach enhances user experience and promotes collaboration by allowing diverse stakeholders to contribute their expertise to the exposome studies.\u003c/p\u003e"},{"header":"3. Project stages development","content":"\u003cp\u003eCHIESS will have four consecutive stages for the development into an informatic platform.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Stage one: Environmental data integration and harmonization system\u003c/h2\u003e \u003cp\u003eThe first stage aims to develop the initial data integration and harmonization components. This stage also involves the development of a data integration application and the processing of queries to allow geospatial operationalization of the general external exposome at the ecosystem (regions) and community (commune) levels. Validation at this stage will consist of determining data inconsistency, integrity, and posttransformation accuracy according to data governance policies and privacy regulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Stage two: Health and omics data integration\u003c/h2\u003e \u003cp\u003eThe second stage focuses on developing complementary informatics components for integrating and harmonizing data from clinical records and biobanks. This will operationalize the specific external exposome at the population level and the internal exposome at the individual level. Validation at this stage will consist of determining data integrity and post-transformation accuracy in addition to confirming the concordance of systems results with well-known geospatial exposure distribution and omics-biomarkers outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Stage three: Development of advanced analytical algorithms\u003c/h2\u003e \u003cp\u003eThe third stage involves developing hypothesis-based or supervised and unsupervised agnostic algorithms which complement causal diagrams. CHiESS is focused on geospatial complex interactions and the trend of multiple expositions analyses. Thus, the Fay-Herriot and Spatial Fay-Herriot models (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), Bayesian structural time series, Bayesian multiple index model, and mixed models with weighted cumulative exposure index will be considered.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Stage four: Visualization interface development and population-based cohort recruitment\u003c/h2\u003e \u003cp\u003eThe fourth stage is focused on developing and implementing visualization and monitoring interfaces for mapping and surveillance hotspots for use by final users and stakeholders. Moreover, the identification of hotspots at this stage will help determine policy recommendations, plan interventions, and recruit a hotspot-targeted population-based cohort for the measurement of environmental stressors and omics as well as a final external validation.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Main challenges","content":"\u003cp\u003eThe challenges for operationalizing human exposome in practice have been previously identified; the totality of environmental exposures is tremendously challenging (\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The challenges include high dimension integration and mining data with time and spatial dependence structure, dealing with heterogeneity data, and development of wearable measurement sensors. Fortunately, with the advancements in bioinformatics, big data analytics, and artificial intelligence, these challenges can be overcome.\u003c/p\u003e \u003cp\u003eThe main challenges for CHiESS are incorporating causal structures in the integrated data, ensuring advanced analytics are appropriately tailored to a robust causal design that tests hypotheses, and understanding the role of confounders. Therefore, epidemiological and clinical reasoning is essential during data integration and analysis. Since CHiESS is initially based on administrative data integration, validating exposure and outcome data measurements is another challenge. The application of known exposure or outcomes biomarkers, complemented by validated questionnaire-based approaches, may overcome this limitation because altered mRNA, proteins, or metabolite levels will reflect specific environmental exposures.\u003c/p\u003e \u003cp\u003eNotwithstanding the aforementioned limitations, matching omics measurements with functionalities (e.g., biomarker development, differentiating exposures from biological responses, investigating mixtures and interactions between agents, and understanding mechanisms for biological plausibility and etiology of diseases) is undoubtedly the major challenge for CHiESS due to its cost. Hence, the prospective cohort study design best suits the exposure biomarker approach, providing opportunities for repeat sampling to enable a broader timeframe of exposure assessment and avoid reverse causation by collecting samples before the disease onset. Thus, CHiESS provides a criteria for defining environmental health priorities, which will guide priority cohort recruitment as well as researching and tailoring omics measurement and target intervention at the population level.\u003c/p\u003e \u003cp\u003eBecause CHIESS is based on a one-health ecosystem approach, another challenge is understanding the particularities of each health problem and establishing connections with general processes such as globalization, territorial expansion, migration, job insecurity, vulnerability of populations, environmental degradation, and urbanization (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, ethical challenges in data integration could arise from the potential mishandling of data, privacy concerns, security risks, data governance, transparency and accountability, legal and regulatory compliance, and the impact on individuals and society. Thus, applying technical measures, legal frameworks, and ethical guidelines will ensure that data integration is conducted responsibly and transparently.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe complexity of human exposome requires to be investigated progressively. To our knowledge, CHiESS is an innovative exposome-based project in LATAM that contextualizes the human exposome and introduces the exposome paradigm in translational research. Unlike other initiatives, the CHiESS model is based on the one-health ecosystem approach; the patterns of other species could contribute to the spread of communicable diseases. Furthermore, CHiESS focuses on the geospatial operationalization of the general external exposome based on the administrative and routinely collected data.\u003c/p\u003e \u003cp\u003eThe novel CHiESS technological platform involves developing automated and semi-automated systems for data integration, advanced analytics and visualization, and monitoring, which is similar to other platforms (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This will facilitate meaningful exposome research and empower researchers and stakeholders to derive significant conclusions from the integrated data and advanced analytics. Nevertheless, it is essential to carry out a proof of concept study to validate the feasibility and effectiveness of data integration and harmonization before it can be fully developed.\u003c/p\u003e \u003cp\u003eIn conclusion, the CHiESS is a novel project that will provide an informatics and intelligent platform that will integrate administrative and original target omics data for exposome studies in Latin America. Most Latin American and developing countries are facing the double burden of diseases. Thus, the CHiESS conceptual model is based on the one-health ecosystem approach. It includes environmental stressors and other species\u0026rsquo; patterns that contribute to the transmission of infectious diseases and the etiology of non-communicable diseases. CHiESS will serve as a basis for targeted, evidence-based interventions, efficient and tailored population-based cohort recruitment, and omics measurements.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol was approved by the Scientific Ethics Committee of the Universidad de los Andes, Chile. Folio number 2024005.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource of Funding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePM, AS, and CU, the study. KP and AS contributed with the technology design CR, MC, FB and MG participated in the manuscript written. \u0026nbsp;CU and PM wrote and edited the manuscripts.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInstitute for Health Metrics and Evaluation (IHME). GBD Compare Data Visualization. Seattle, WA: IHME, University of Washington., 2020. Available from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://vizhub.healthdata.org/gbd-compare\u003c/span\u003e\u003cspan address=\"http://vizhub.healthdata.org/gbd-compare\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (Accessed [September, 2023]).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOng KL, Stafford LK, McLaughlin SA, Boyko EJ, Vollset SE, Smith AE, et al. 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Salud Colectiva. 2018;14:1\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrook JR, Setton EM, Seed E, Shooshtari M, Doiron D, Awadalla P, et al. The Canadian Urban Environmental Health Research Consortium \u0026ndash; a protocol for building a national environmental exposure data platform for integrated analyses of urban form and health. BMC Public Health. 2018;18(1):114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerino Martinez R, M\u0026uuml;ller H, Negru S, Ormenisan A, Arroyo M\u0026uuml;hr LS, Zhang X, et al. Human exposome assessment platform. Environ Epidemiol. 2021;5(6):e182.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Exposome, Data Integration, Ecosystems, Systems of system","lastPublishedDoi":"10.21203/rs.3.rs-3914159/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3914159/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe double burden of diseases and scarce resources in developing countries highlight the need to change conceptualization of health problems and development of translational research. Contrary to the traditional paradigm focused on genetics, the exposome approach 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\u0026rsquo;s environment throughout their life and health.\u003c/p\u003e \u003cp\u003eHerein, we have describe the conceptual model and technological system development of the Chilean exposome-based system for ecosystems (CHiESS). It is an intelligent and dynamic system for human exposome research, which leverages available administrative data routinely collected by national agencies, in clinical records, and by biobanks. Based on the ecological theory and one-health ecosystem approach, 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.\u003c/p\u003e \u003cp\u003eThe 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 exosome-based hotspots, CHiESS allows the targeted and efficient recruitment of population-based cohorts for translational research and impact evaluation.\u003c/p\u003e","manuscriptTitle":"The Chilean exposome-based system for ecosystems project: Protocol for the development of an informatics platform for national data integration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-06 17:27:09","doi":"10.21203/rs.3.rs-3914159/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e7ef52c-1c66-49d5-bcbb-798ba5b39598","owner":[],"postedDate":"February 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-26T12:01:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-06 17:27:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3914159","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3914159","identity":"rs-3914159","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}