Agent-based modeling of urban exposome interventions: prospects, model architectures, and methodological challenges

Advancing translational exposomics: bridging genome, exposome and personalized medicine

Understanding the interplay between genetic predisposition and environmental and lifestyle exposures is essential for advancing precision medicine and public health. The exposome, defined as the sum of all environmental exposures an individual encounters throughout their lifetime, complements genomic data by elucidating how external and internal exposure factors influence health outcomes. This treatise highlights the emerging discipline of translational exposomics that integrates exposomics and genomics, offering a comprehensive approach to decipher the complex relationships between environmental and lifestyle exposures, genetic variability, and disease phenotypes. We highlight cutting-edge methodologies, including multi-omics technologies, exposome-wide association studies (EWAS), physiology-based biokinetic modeling, and advanced bioinformatics approaches. These tools enable precise characterization of both the external and the internal exposome, facilitating the identification of biomarkers, exposure-response relationships, and disease prediction and mechanisms. We also consider the importance of addressing socio-economic, demographic, and gender disparities in environmental health research. We emphasize how exposome data can contextualize genomic variation and enhance causal inference, especially in studies of vulnerable populations and complex diseases. By showcasing concrete examples and proposing integrative platforms for translational exposomics, this work underscores the critical need to bridge genomics and exposomics to enable precision prevention, risk stratification, and public health decision-making. This integrative approach offers a new paradigm for understanding health and disease beyond genetics alone.

The epidemiologic case for urban health: conceptualizing and measuring the magnitude of challenges and potential benefits

We discuss how epidemiology has been and can continue to be used to advance understanding of the links between urban areas and health informed by an existing urban-health conceptual framework. This framework considers urban areas as contexts for health, determinants of health and modifiers of health pathways, and part of a complex system that affects health. We highlight opportunities for descriptive epidemiology to inform the context of urban health, for example, by characterizing the social and physical environments that give rise to health and the actions that change those conditions. We then describe inferential tools for evaluating the impact of group-level actions (e.g., interventions, policies) on urban health, providing some examples, and describing assumptions and challenges. Finally, we discuss opportunities and challenges of applying systems thinking and methods to advance urban health. While different conceptual frames lead to different insights, each perspective demonstrates that urban health is a major and growing challenge. The effectiveness of urban health knowledge, action, and policy as the world continues to urbanize can be informed by applying and expanding upon research and surveillance methods described here.

Global research trends on the human exposome: a bibliometric analysis (2005-2024)

Exposome represents one of the most pressing issues in the environmental science research field. However, a comprehensive summary of worldwide human exposome research is lacking. We aimed to explore the bibliometric characteristics of scientific publications on the human exposome. A bibliometric analysis of human exposome publications from 2005 to December 2024 was conducted using the Web of Science in accordance with PRISMA guidelines. Trends/hotspots were investigated with keyword frequency, co-occurrence, and thematic map. Sex disparities in terms of publications and citations were examined. From 2005 to 2024, 931 publications were published in 363 journals and written by 4529 authors from 72 countries. The number of publications tripled during the last 5 years. Publications written by females (51% as first authors and 34% as last authors) were cited fewer times (13,674) than publications written by males (22,361). Human exposome studies mainly focused on air pollution, metabolomics, chemicals (e.g., per- and polyfluoroalkyl substances (PFAS), endocrine-disrupting chemicals, pesticides), early-life exposure, biomarkers, microbiome, omics, cancer, and reproductive disorders. Social and built environment factors, occupational exposure, multi-exposure, digital exposure (e.g., screen use), climate change, and late-life exposure received less attention. Our results uncovered high-impact countries, institutions, journals, references, authors, and key human exposome research trends/hotspots. The use of digital exposome technologies (e.g., sensors, and wearables) and data science (e.g., artificial intelligence) has blossomed to overcome challenges and could provide valuable knowledge toward precision prevention. Exposome risk scores represent a promising research avenue.

Assessing the Impact and Cost-Effectiveness of Exposome Interventions on Alzheimer's Disease: A Review of Agent-Based Modeling and Other Data Science Methods for Causal Inference

Background: The exposome (e.g., totality of environmental exposures) and its role in Alzheimer's Disease and Alzheimer's Disease and Related Dementias (AD/ADRD) are increasingly critical areas of study. However, little is known about how interventions on the exposome, including personal behavioral modification or policy-level interventions, may impact AD/ADRD disease burden at the population level in real-world settings and the cost-effectiveness of interventions. Methods: We performed a critical review to discuss the challenges in modeling exposome interventions on population-level AD/ADRD burden and the potential of using agent-based modeling (ABM) and other advanced data science methods for causal inference to achieve this. Results: We describe how ABM can be used for empirical causal inference modeling and provide a virtual laboratory for simulating the impacts of personal and policy-level interventions. These hypothetical experiments can provide insight into the optimal timing, targeting, and duration of interventions, identifying optimal combinations of interventions, and can be augmented with economic analyses to evaluate the cost-effectiveness of interventions. We also discuss other data science methods, including structural equation modeling and Mendelian randomization. Lastly, we discuss challenges in modeling the complex exposome, including high dimensional and sparse data, the need to account for dynamic changes over time and over the life course, and the role of exposome burden scores developed using item response theory models and artificial intelligence to address these challenges. Conclusions: This critical review highlights opportunities and challenges in modeling exposome interventions on population-level AD/ADRD disease burden while considering the cost-effectiveness of different interventions, which can be used to aid data-driven policy decisions.

Reconceptualizing and Defining Exposomics within Environmental Health: Expanding the Scope of Health Research

BACKGROUND

Exposomics, the study of the exposome, is flourishing, but the field is not well defined. The term "exposome" refers to all environmental influences and associated biological responses throughout the lifespan. However, this definition is very similar to that of the term "environment"-the external elements and conditions that surround and affect the life and development of an organism. Consequently, the exposome seems to be nothing more than a synonym for the environment, and exposomics a synonym for environmental research. As a result, some have rebranded their "standard" environmental health research with the neologistic exposome term, whereas others ignore or seek to abandon the seemingly redundant concept of the exposome.

OBJECTIVES

We argue that exposomics needs to sharpen its mission focus to counteract this apparent redundancy. Exposomics should be defined as a research program in environmental health aimed at enabling a comprehensive and discovery-driven approach to identifying environmental determinants of human health. Similar to the aim of the Human Genome Project, exposomics aims to analyze the complete complexity of exposures and their corresponding biological responses. Exposomics' primary premise is that the existence of undiscovered, potentially interconnected, nongenetic (environmental) risk factors for health necessitates a comprehensive discovery-driven analysis approach.

DISCUSSION

We argue that exposomics researchers should adopt our reconceptualization of exposomics and focus on the productiveness and integrity of their research program: its purpose and principles. We suggest that exposomics researchers should coordinate the writing of reviews that assess the program's productiveness and integrity, as well as provide a platform for exposomics researchers to define their vision for the field. https://doi.org/10.1289/EHP14509.

Current limitations and opportunities for improvements of agent-based transport models for noise exposure assessment

Agent-based models represent a promising approach for simulating transport systems and assessing their environmental noise impact, potentially enhancing standard noise exposure assessments. However, it is very important to understand the relevance of these assessments within the context of models initially designed for transport studies. Then, this research investigates the utilization of agent-based transport models when coupled with environmental models to assess individual exposure to transport-related noise. This is achieved by proposing a method to evaluate this approach across four dimensions: spatial, temporal, individual, and activity patterns. This evaluation is demonstrated and discussed with an exemplification model applied in the Lyon Metropolitan Area using open-source tools (MATSim, EQASim, NoiseModelling), which is a representative framework of the current literature. The findings encompass a range of issues, including the conceptualization of exposure contexts and activity spaces, the resolution of the acoustic content, the disaggregation of data at the individual level, the variability in noise reactions, and the correlation structures between social and exposure profiles. The study contributes to the advancement of exposure assessment with insights for future improvements in the field. Further, it underscores the need for more quantitative analyses and scientific research into momentary noise exposure and social epidemiology.

Current topics and challenges in geoAI

Taken literally, geoAI is the use of Artificial Intelligence methods and techniques in solving geo-spatial problems. Similar to AI more generally, geoAI has seen an influx of new (big) data sources and advanced machine learning techniques, but also a shift in the kind of problems under investigation. In this article, we highlight some of these changes and identify current topics and challenges in geoAI.