Exposome最新文献

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.

Background: Studies of factors influencing body mass index (BMI) mostly focus on hypothesized/established factors. We aimed to explore potential factors influencing BMI across childhood and adolescence using a hypothesis-free exposome-wide association study (ExWAS), with external replication and Mendelian randomization (MR).

Methods: The ExWAS was done in the Avon Longitudinal Study of Parents and Children (ALSPAC) using univariable regressions to estimate prospective associations of 2,523 exposures with BMI in infancy (up to 12 months, N = 12 761), early-childhood (13-60 months, N = 12 138), middle-childhood (61 months to 12 years, N = 9,105) and adolescence (13-18 years, N = 7379). Associations passing Bonferroni correction were followed-up with confounder-adjusted regression in ALSPAC. Replication of confounder-adjusted associations were explored in eight independent cohorts and/or MR analyses in ALSPAC.

Results: The ExWAS identified 8, 16, 66 and 82 exposures prospectively associated with BMI in infancy, early-childhood, middle-childhood, and adolescence, respectively. Of these, 8, 11, 42, and 26 remained associated following confounder adjustment. Replicated associations included maternal BMI, smoking and fat intake; only maternal smoking was supported by MR. Associations of more TV viewing time with higher BMI at one or more ages was supported by MR showing more time spent watching TV in early-childhood leads to higher BMI in middle-childhood. It was not possible to explore replication or MR for confounder-adjusted associations of offspring emotion, sociability, and peer problems with subsequent BMI.

Conclusions: Our ExWAS suggests more TV viewing time in early-childhood increases BMI in mid-childhood. Novel potential effects of social, emotional and peer exposures in childhood need replication and assessment of causality.

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.

The correlations among individual exposures in the exposome, which refers to all exposures an individual encounters throughout life, are important for understanding the landscape of how exposures co-occur, and how this impacts health and disease. Exposome-wide association studies (ExWAS), which are analogous to genome-wide association studies (GWAS), are increasingly being used to elucidate links between the exposome and disease. Despite increased interest in the exposome, tools and publications that characterize exposure correlations and their relationships with human disease are limited, and there is a lack of data and results sharing in resources like the GWAS catalog. To address these gaps, we developed the PEGS Explorer web application to explore exposure correlations in data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS) that were rigorously calculated to account for differing data types and previously published results from ExWAS. Through globe visualizations, PEGS Explorer allows users to explore correlations between exposures found to be associated with complex diseases. The exposome data used for analysis includes not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The web application addresses the lack of accessible data and results sharing, a major challenge in the field, and enables users to put results in context, generate hypotheses, and, importantly, replicate findings in other cohorts. PEGS Explorer will be updated with additional results as they become available, ensuring it is an up-to-date resource in exposome science.

The exposome collectively refers to all exposures, beginning in utero and continuing throughout life, and comprises not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The exposome interacts with individual genetic and epigenetic characteristics to affect human health and disease, but large-scale studies that characterize the exposome and its relationships with human disease are limited. To address this gap, we used extensive questionnaire data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS, n = 9, 429) to evaluate exposure associations in relation to common diseases. We performed an exposome-wide association study (ExWAS) to examine single exposure models and their associations with 11 common complex diseases, namely allergic rhinitis, asthma, bone loss, fibroids, high cholesterol, hypertension, iron-deficient anemia, ovarian cysts, lower GI polyps, migraines, and type 2 diabetes. Across diseases, we found associations with lifestyle factors and socioeconomic status as well as asbestos, various dust types, biohazardous material, and textile-related exposures. We also found disease-specific associations such as fishing with lead weights and migraines. To differentiate between a replicated result and a novel finding, we used an AI-based literature search and database tool that allowed us to examine the current literature. We found both replicated findings, especially for lifestyle factors such as sleep and smoking across diseases, and novel findings, especially for occupational exposures and multiple diseases.