Exposome最新文献

Childhood obesity is an increasingly severe public health problem, with a prospective impact on health. We propose an exposome approach to identify actionable risk factors for this condition. Our assumption is that relationships between external exposures and outcomes such as rapid growth, overweight, or obesity in children can be better understood through a "meet-in-the-middle" model. This is based on a combination of external and internal exposome-based approaches, that is, the study of multiple exposures (in our case, dietary patterns) and molecular pathways (metabolomics and epigenetics). This may strengthen causal reasoning by identifying intermediate markers that are associated with both exposures and outcomes. Our biomarker-based studies in the STOP consortium suggest (in several ways, including mediation analysis) that branched-chain amino acids (BCAAs) could be mediators of the effect of dietary risk factors on childhood overweight/obesity. This is consistent with intervention and animal studies showing that higher intake of BCAAs has a positive impact on body composition, glycemia, and satiety. Concerning food, of particular concern is the trend of increasing intake of ultra-processed food (UPF), including among children. Several mechanisms have been proposed to explain the impact of UPF on obesity and overweight, including nutrient intake (particularly proteins), changes in appetite, or the role of additives. Research from the Avon Longitudinal Study of Parents and Children cohort has shown a relationship between UPF intake and trajectories in childhood adiposity, while UPF was related to lower blood levels of BCAAs. We suggest that an exposome-based approach can help strengthening causal reasoning and support policies. Intake of UPF in children should be restricted to prevent obesity.

In recent years, exposome research has been put forward as the next frontier for the study of human health and disease. Exposome research entails the analysis of the totality of environmental exposures and their corresponding biological responses within the human body. Increasingly, this is operationalized by big-data approaches to map the effects of internal as well as external exposures using smart sensors and multiomics technologies. However, the ethical implications of exposome research are still only rarely discussed in the literature. Therefore, we conducted a systematic review of the academic literature regarding both the exposome and underlying research fields and approaches, to map the ethical aspects that are relevant to exposome research. We identify five ethical themes that are prominent in ethics discussions: the goals of exposome research, its standards, its tools, how it relates to study participants, and the consequences of its products. Furthermore, we provide a number of general principles for how future ethics research can best make use of our comprehensive overview of the ethical aspects of exposome research. Lastly, we highlight three aspects of exposome research that are most in need of ethical reflection: the actionability of its findings, the epidemiological or clinical norms applicable to exposome research, and the meaning and action-implications of bias.

Environmental exposures have been linked to COVID-19 severity. Previous studies examined very few environmental factors, and often only separately without considering the totality of the environment, or the exposome. In addition, existing risk prediction models of severe COVID-19 predominantly rely on demographic and clinical factors. To address these gaps, we conducted a spatial and contextual exposome-wide association study (ExWAS) and developed polyexposomic scores (PES) of COVID-19 hospitalization leveraging rich information from individuals' spatial and contextual exposome. Individual-level electronic health records of 50 368 patients aged 18 years and older with a positive SARS-CoV-2 PCR/Antigen lab test or a COVID-19 diagnosis between March 2020 and October 2021 were obtained from the OneFlorida+ Clinical Research Network. A total of 194 spatial and contextual exposome factors from 10 data sources were spatiotemporally linked to each patient based on geocoded residential histories. We used a standard two-phase procedure in the ExWAS and developed and validated PES using gradient boosting decision trees models. Four exposome measures significantly associated with COVID-19 hospitalization were identified, including 2-chloroacetophenone, low food access, neighborhood deprivation, and reduced access to fitness centers. The initial prediction model in all patients without considering exposome factors had a testing-area under the curve (AUC) of 0.778. Incorporation of exposome data increased the testing-AUC to 0.787. Similar findings were observed in subgroup analyses focusing on populations without comorbidities and aged 18-24 years old. This spatial and contextual exposome study of COVID-19 hospitalization confirmed previously reported risk factor but also generated novel predictors that warrant more focused evaluation.

The health and disease of an individual is mediated by their genetics, a lifetime of environmental exposures, and interactions between the two. Genetic or biological sex, including chromosome composition and hormone expression, may influence both the types and frequency of environmental exposures an individual experiences, as well as the biological responses an individual has to those exposures. Gender identity, which can be associated with social behaviors such as expressions of self, may also mediate the types and frequency of exposures an individual experiences. Recent advances in exposome-level analysis have progressed our understanding of how environmental factors affect health outcomes; however, the relationship between environmental exposures and sex- and gender-specific health remains underexplored. The comprehensive, non-targeted, and unbiased nature of exposomic research provides a unique opportunity to systematically evaluate how environmental exposures interact with biological sex and gender identity to influence health. In this forward-looking narrative review, we provide examples of how biological sex and gender identity influence environmental exposures, discuss how environmental factors may interact with biological processes, and highlight how an intersectional approach to exposomics can provide critical insights for sex- and gender-specific health sciences.

The accumulation of every day exposures can impact health across the life course, but our understanding of such exposures is impeded by our ability to delineate the relationship between an individual's early life exposome and later life health effects. Measuring the exposome is challenging. Exposure assessed at a given time point captures a snapshot of the exposome but does not represent the full spectrum of exposures across the life course. In addition, the assessment of early life exposures and their effects is often further challenged by lack of relevant samples and the time gap between exposures and related health outcomes in later life. Epigenetics, specifically DNA methylation, has the potential to overcome these barriers as environmental epigenetic perturbances can be retained through time. In this review, we describe how DNA methylation can be framed in the world of the exposome. We offer three compelling examples of common environmental exposures, including cigarette smoke, the endocrine active compound bisphenol A (BPA), and the metal lead (Pb), to illustrate the application of DNA methylation as a proxy to measure the exposome. We discuss areas for future explorations and current limitations of this approach. Epigenetic profiling is a promising and rapidly developing tool and field of study, offering us a unique and powerful way to assess the early life exposome and its effects across different life stages.

Primary sclerosing cholangitis (PSC) is a complex bile duct disorder. Its etiology is incompletely understood, but environmental chemicals likely contribute to risk. Patients with PSC have an altered bile metabolome, which may be influenced by environmental chemicals. This novel study utilized state-of-the-art high-resolution mass spectrometry (HRMS) with bile samples to provide the first characterization of environmental chemicals and metabolomics (collectively, the exposome) in PSC patients located in the United States of America (USA) (n = 24) and Norway (n = 30). First, environmental chemical- and metabolome-wide association studies were conducted to assess geographic-based similarities and differences in the bile of PSC patients. Nine environmental chemicals (false discovery rate, FDR < 0.20) and 3143 metabolic features (FDR < 0.05) differed by site. Next, pathway analysis was performed to identify metabolomic pathways that were similarly and differentially enriched by the site. Fifteen pathways were differentially enriched (P < .05) in the categories of amino acid, glycan, carbohydrate, energy, and vitamin/cofactor metabolism. Finally, chemicals and pathways were integrated to derive exposure-effect correlation networks by site. These networks demonstrate the shared and differential chemical-metabolome associations by site and highlight important pathways that are likely relevant to PSC. The USA patients demonstrated higher environmental chemical bile content and increased associations between chemicals and metabolic pathways than those in Norway. Polychlorinated biphenyl (PCB)-118 and PCB-101 were identified as chemicals of interest for additional investigation in PSC given broad associations with metabolomic pathways in both the USA and Norway patients. Associated pathways include glycan degradation pathways, which play a key role in microbiome regulation and thus may be implicated in PSC pathophysiology.

With ever more people living in cities worldwide, it becomes increasingly important to understand and improve the impact of the urban habitat on livability, health behaviors, and health outcomes. However, implementing interventions that tackle the exposome in complex urban systems can be costly and have long-term, sometimes unforeseen, impacts. Hence, it is crucial to assess the health impact, cost-effectiveness, and social distributional impacts of possible urban exposome interventions (UEIs) before implementing them. Spatial agent-based modeling (ABM) can capture complex behavior-environment interactions, exposure dynamics, and social outcomes in a spatial context. This article discusses model architectures and methodological challenges for successfully modeling UEIs using spatial ABM. We review the potential and limitations of the method; model components required to capture active and passive exposure and intervention effects; human-environment interactions and their integration into the macro-level health impact assessment and social costs benefit analysis; and strategies for model calibration. Major challenges for a successful application of ABM to UEI assessment are (1) the design of realistic behavioral models that can capture different types of exposure and that respond to urban interventions, (2) the mismatch between the possible granularity of exposure estimates and the evidence for corresponding exposure-response functions, (3) the scalability issues that emerge when aiming to estimate long-term effects such as health and social impacts based on high-resolution models of human-environment interactions, (4) as well as the data- and computational complexity of calibrating the resulting agent-based model. Although challenges exist, strategies are proposed to improve the implementation of ABM in exposome research.

The concept of heritability parses out genetic and environmental causes of diseases and does not fit the underlying biology of complex diseases that arise from interactions among genetics and environment. Exposomics places environment on a similar scale as genomics and allows for more modern research approaches that estimate time-varying genome by exposome interactions. By addressing the biological underpinnings of disease comprehensively, we will find the "missing heritability" which is not solely based on genetic variation but is instead driven by time, life stage, and geographic variability in our exposome as it interacts with our genome.