Exposome最新文献

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.

Extracellular vesicles (EVs) released by neurons (nEVs) provide an opportunity to measure biomarkers from the brain circulating in the periphery. No study yet has directly compared molecular cargo in brain tissue to nEVs found in circulation in humans. In 5 matched sets of brain tissue, serum, total EVs, and nEVs, obtained from the Bartoli Brain Tumor Laboratory at Columbia University, we compared the levels microRNAs and environmental chemicals because microRNAs are one of the most studied nEV cargoes and offer great potential as biomarkers and environmental chemical load in nEVs is understudied and could reveal chemical burden in the brain. We also compared metabolomic profiles in a different set of matched serum, total EVs, and nEVs since metabolites in nEVs are also understudied but could offer potential biomarkers. Highly expressed brain tissue miRNAs showed stronger correlations with nEVs than serum or total EVs. We detected several environmental chemical pollutant classes in nEVs. The chemical pollutant concentrations in nEVs were more strongly correlated with brain tissue levels (r = 0.72, P = 7.2e-16) than those observed between brain tissue and serum (r = 0.7, P = 5.8e-15) or total EVs (r = 0.58, P = 1.5e-09). Compared to serum and total EVs, we observed an enrichment of metabolites with known signaling roles, such as bile acids, oleic acid, phosphatidylserine, and isoprenoids in nEVs. We provide evidence that nEV cargo is closely correlated with brain tissue content, further supporting their utility as a brain liquid biopsy in humans.

Cognitive impairment among older adults is a growing public health challenge and environmental chemicals may be modifiable risk factors. A wide array of chemicals has not yet been tested for association with cognition in an environment-wide association framework. In the US National Health and Nutrition Examination Survey (NHANES) 1999-2000 and 2011-2014 cross-sectional cycles, cognition was assessed using the Digit Symbol Substitution Test (DSST, scores 0-117) among participants aged 60 years and older. Concentrations of environmental chemicals measured in blood or urine were log₂ transformed and standardized. Chemicals with at least 50% of measures above the lower limit of detection were included (n_chemicals = 147, n_classes=14). We tested for associations between chemical concentrations and cognition using parallel survey-weighted multivariable linear regression models adjusted for age, sex, race/ethnicity, education, smoking status, fish consumption, cycle year, urinary creatinine, and cotinine. Participants with at least one chemical measurement (n = 4982) were mean age 69.8 years, 55.0% female, 78.2% non-Hispanic White, and 77.0% at least high school educated. The mean DSST score was 50.4 (standard deviation (SD)=17.4). In adjusted analyses, 5 of 147 exposures were associated with DSST at P-value <.01. Notably, a SD increase in log₂-scaled cotinine concentration was associated with 2.71 points lower DSST score (95% CI -3.69, -1.73). A SD increase in log₂-scaled urinary tungsten concentration was associated with 1.34 points lower DSST score (95% CI -2.11, -0.56). Exposure to environmental chemicals, particularly metals and tobacco smoke, may be modifiable factors for cognition among older adults.

The exposome was proposed following the realization that most human diseases have an environmental rather than a genetic (hereditary) origin. Non-communicable diseases are, in fact, the consequence of multiple exposures that activate a sequence of stages in a multistage process that already starts in early life. This attracted attention to both the multiplicity (in fact, potentially the totality) of exposures humans are exposed to since conception and to the life-long perspective of disease causation. In this paper, we examine an extension of the exposome concept that incorporates a Darwinian approach based on the concept of phenotypic plasticity. One of the theses is that interpreting exposome science as "precision environmental research" is only a partial interpretation, largely focused on chemical exposures, while a broadening of the perspective is needed, also in light of the planetary crisis. Such broadening involves the incorporation of basic concepts from evolutionary biology and medicine, including the ability of organisms to adapt to rapidly changing environments. We refer in particular to cancer and "Darwinian carcinogenesis."

The exposome concept aims to account for the comprehensive and cumulative effects of physical, chemical, biological, and psychosocial influences on biological systems. To date, limited exposome research has explicitly included climate change-related exposures. We define these exposures as those that will intensify with climate change, including direct effects like extreme heat, tropical cyclones, wildfires, downstream effects like air pollution, power outages, and limited or contaminated food and water supplies. These climate change-related exposures can occur individually or simultaneously. Here, we discuss the concept of a climate mixture, defined as three or more simultaneous climate change-related exposures, in the context of the exposome. In a motivating climate mixture example, we consider the impact of a co-occurring tropical cyclone, power outage, and flooding on respiratory hospitalizations. We identify current gaps and future directions for assessing the effect of climate mixtures on health. Mixtures methods allow us to incorporate climate mixtures into exposomics.