Exposome最新文献

Background: The rapid evolution and diversity of sensor technologies, coupled with inconsistencies in how sensor metadata is reported across formats and sources, present significant challenges for generating exposomes and exposure health research.

Objective: Despite the development of standardized metadata schemas, the process of extracting sensor metadata from unstructured sources remains largely manual and unscalable. To address this bottleneck, we developed and evaluated a large language model (LLM)-based pipeline for automating sensor metadata extraction and harmonization from publicly available exposure health literature.

Methods: Using GPT-4 in a zero-shot setting, we constructed a pipeline that parses full-text PDFs to extract metadata and harmonizes output into structured formats.

Results: Our automated pipeline achieved substantial efficiency gains in completing extractions much faster than manual review and demonstrated strong performance with 88.0% accuracy, 88.0% precision, 93.0% recall, and an F1-score of 90.0%.

Conclusions: This study demonstrates the feasibility and scalability of leveraging LLMs to automate sensor metadata extraction for exposure health, reducing manual burden while enhancing metadata completeness and consistency. Our findings support the integration of LLM-driven pipelines into exposure health informatics platforms.

Hypertensive disorders of pregnancy (HDP) are a leading cause of maternal and perinatal morbidity, yet modifiable environmental risk factors remain poorly characterized. Prior studies typically have only examined a limited number of exposures and have rarely distinguished HDP subtypes (ie, gestational hypertension, preeclampsia, eclampsia, and chronic hypertension with or without superimposed preeclampsia) or accounted for residential mobility during pregnancy. To address these gaps, we conducted a spatial and contextual exposome study of HDP using linked electronic health records (EHR) and vital statistics data in Florida. We analyzed 686 412 singleton pregnancies conceived between 2013 and 2018, using computable phenotyping to distinguish HDP subtypes. A total of 245 spatial and contextual exposome measures spanning natural, built, and social environments were linked to residential histories from conception through gestational week 19. Using a two-phase double machine learning (DML) framework with exposure-specific, directed acyclic graph-guided confounder adjustment, we conducted discovery and replication analyses, followed by multi-treatment DML to estimate effect sizes. In Phase 1, 26 exposome measures replicated for gestational hypertension and 34 for overall HDP. In Phase 2, 12 measures remained associated with gestational hypertension and 11 with overall HDP, including air toxicants, meteorological factors, ultraviolet radiation, neighborhood crime indicators, environmental noise, and proximity to coastline. No exposures passed multiple-comparison thresholds for preeclampsia or eclampsia. These findings demonstrate that the spatial and contextual exposome contributes to HDP in a subtype-specific manner. Integrating EHR-linked phenotyping, residential mobility, and causal machine-learning methods offers a scalable framework for identifying environmental factors relevant to HDP prevention.

Historically and willingly, the exposome framework focuses on human health. Ecosystems are considered only partially and are always envisioned as part of the exposures. As knowledge expands on the human exposome, human-nature relationships and interdependency are becoming increasingly obvious, with an impact of nature on human health and well-being. Including ecosystems into the exposome framework would help to identify shared exposures, boomerang effects, specific adaptations occurring in other living beings, nature-based solutions to mitigate identified problematics linked to human health, and maintain a habitable environment for all living beings.

Wearable passive samplers present tremendous opportunity for measuring the exposome. These tools offer a user-friendly and cost-effective approach to assess personal exposure, enabling access to remote communities and under-researched vulnerable populations. Use of these wearable devices has gained popularity over the past two decades but before these tools can be adopted for routine use in large-scale health studies, they require standardization to ensure data reproducibility. With the objective of harmonizing wearable passive sampler use, this paper serves as a guide to available technologies (Fresh Air samplers, commercial silicone wristbands, custom silicone bands, and the silicone brooch) and best practices for assessing exposure to environmental chemicals. We discuss the optimal duration to wear these sampling tools, essential quality control and quality assurance measures, chemical analysis approaches, and strategies to interpret measured exposures. We also propose minimum guidelines for sample collection and reporting data to foster measurement standardization and secondary analysis. Strategies to improve data transparency through harmonization will enable comparability of the comprehensive measures of the exposome using wearable passive samplers.

Although bisphenol F (BPF) is widely used in plastic products, there are concerns about its potential health risks. Here, we aimed to understand the long-term effects of a brief BPF exposure during development. We treated zebrafish larvae 7 days post-fertilization with 1 mg/L BPF for 48 hours. We then maintained the animals under standard conditions for 5 months and compared them to control adults never exposed to BPF. In addition to sequencing the gut microbiome, we profiled six different tissues and serum by metabolomics and lipidomics. Strikingly, we found widespread alterations in metabolites and lipids throughout the animal that were both sex and tissue specific. For example, over 60 lipid species were depleted in the livers of BPF-treated females but no changes were observed in male livers. At the tissue level, BPF treatment altered fatty acid oxidation uniquely in skeletal and cardiac muscle. This study shows that transient exposures limited to the developmental phase of life can induce metabolic abnormalities later in adulthood. Our findings highlight the importance of profiling specimens from early life by exposomics and suggest that, even with the introduction of regulatory measures, the adverse effects of BPF could persist in the population for a generation.

Environmental exposures are contributors to morbidity and mortality, yet the potential protective role of health insurance in mitigating these effects remains underexplored. We used data from the 1999-2000 and 2001-2002 cycles of the National Health and Nutrition Examination Survey (NHANES) to examine whether health insurance status is associated with the chemical exposome and whether being insured modifies relationships of environmental exposures with epigenetic aging biomarkers of morbidity and mortality (GrimAge2 and DunedinPoAm). Among 2315 adults aged 50 - 84 years, we evaluated 64 chemical exposures. In covariate, socioeconomic status-adjusted models, being insured compared to being uninsured was marginally associated with 0.21-SD lower blood lead levels (95% CI: -0.39, -0.04, P = 0.03) and 0.29-SD higher PCB180 levels (95% CI: 0.003, 0.57, P = 0.048). In leukocyte- and covariate-adjusted models, insurance attenuated the relationship of lead with GrimAge2 (Insured: β = 0.08-years, 95% CI: -0.08, 0.24; Uninsured β = 0.65-years, 95% CI: 0.11, 1.20; P _interaction = 0.04) and DunedinPoAm (Insured: β = 0.001, 95% CI: -0.002, 0.003; Uninsured β = 0.01, 95% CI 0.0001, 0.02; P _interaction = 0.047). Similar trends were also observed for cadmium, cotinine, and PCB180 but not statistically different between insurance categories. These findings suggest that health insurance may serve as a protective factor against the biological aging impacts of certain environmental exposures, possibly through improved access to exposure monitoring and preventive/therapeutic care. While not a substitute for environmental policy or exposure remediation, insurance may represent a small, complementary, and more immediately actionable tool to help reduce harm. However, given the small magnitude of model estimates, results should be interpreted with caution regarding their practical significance.

Precision environmental health (PEH) is an emerging field that seeks to understand how diverse environmental exposures interact with individual biological and genetic factors to influence health outcomes. While recent advances in exposomics have enabled systematic characterization of the exposome, the integrated compilation of all physical, chemical, biological, and psychosocial influences that affect biology and health, identifying and developing sensitive biomarkers remains a critical challenge. The human eye offers unique potential for non-invasive biomarker discovery. Ocular biomarkers can be utilized not only for diagnostics and therapeutic responses of ocular diseases, but also for monitoring environmental exposures and predicting systemic health outcomes. Retinal imaging modalities such as color fundus photography, optical coherence tomography, and optical coherence tomography angiography capture biomarkers linked to environmental exposures and systemic conditions like cardiovascular and neurodegenerative diseases, a field known as oculomics. Similarly, ocular fluids, such as tears, aqueous humor and vitreous humor, may also reflect pollution-induced oxidative stress and inflammation and systemic health conditions. This paper summarizes current evidence on how ocular biomarkers can bridge environmental exposures and systemic health outcomes, and proposes future research directions using state of the art methodologies such as exposome-wide association studies, high dimensional mediation analysis, and multi-modal foundation models. Despite encouraging progress, significant challenges remain, including the need for large and standardized datasets, rigorous validation, and ethical safeguards to ensure equitable application. Advances in artificial intelligence, including federated learning, alongside global consortium efforts, will be essential to overcome these barriers. Addressing these gaps will unlock the full potential of oculomics and exposomics, advancing the goals of precision environmental health.

It has been proposed that the measurement of the sequence of exposures an individual is exposed to over time (or "expotype," complementary to the genotype) would be a way to promote precision medicine at the patient's bed, and also primary prevention. The incorporation of new technologies, like omics, genotypes, Electronic Health Records, georeferencing and AI, into public health is attractive; however, the thesis of this Commentary is that the use of the exposome approach for precision prevention needs to be examined critically. The use of the expotype for practical purposes requires proof of causality, and the added value of the expotype may be limited, for example if measured through the NNT (Number Needed to Treat). In addition, the medical system may not afford the extra budgets to measure the expotype at the patient's bed (particularly if it goes beyond the anamnesis and georeferencing, and includes omic measurements). I also argue that public health is largely a matter of structural interventions at the societal level, like taxation, and not only of individual responsibility. The main successes in tackling diseases have been tobacco taxation, sugar taxes and of course vaccination, rather than individualized health promotion. The proposal of extending precision therapy to precision prevention should not divert our attention from the great opportunities for prevention at the population level. Population prevention is cheaper, it usually addresses several diseases with a single intervention (think of smoking or air pollution) and does not need to be replicated at each generation like cure.

Humans are exposed to upwards of thousands of chemicals simultaneously, but research has traditionally focused on the health effects of only one chemical at a time. Single-chemical analyses not only underestimate total health risk, but also ignore bias from multicollinearity and co-exposure confounding between chemicals. Advanced statistical mixture methods address these challenges and allow us to both estimate the cumulative health effect of chemical mixtures and identify the strongest chemical contributors. At the same time, untargeted chemical exposome profiling through high-resolution mass spectrometry (HRMS) now supports measurement of over 100,000 chemical signals in biospecimens. However, most mixture methods cannot evaluate untargeted exposome data containing more chemical variables than samples. Weighted quantile sum regression with its recent random subsets implementation (WQS_RS) is a unique, statistically powerful mixture method for high-dimensional exposome data. It estimates weights of chemicals towards the mixture index over many different repetitions in which only a small random subset of chemicals is used at a time, thus de-correlating data and avoiding overfitting. In this paper, we discuss our statistical workflow and important considerations for the application of WQS_RS to exposome epidemiology, including manual quantization for non-detects, custom repeated holdouts for matched data, pre-selection of exogenous chemicals, parameter decisions, interpretation options, and visualizations. We then describe its application to functional pathway enrichment analysis with integrated exposome-metabolome data to explore underlying biological mechanisms. These data science approaches will enable exposome epidemiology to discover previously unknown risk factors, estimate cumulative health risk from total chemical mixtures, and gain mechanistic insight.