How will Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and lead (Pb) exposure interact in the cardiovascular system?

The combined effects of lead exposure and the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and how they cause cardiovascular dysfunction are examined in this viewpoint manuscript. Lead was picked as a representative environmental contaminant because it has been studied extensively for more than 40 years, while SARS-CoV-2 illustrates the unpredictability of a new infectious agent that can quickly increase the environmental burden on communities. Given that this interaction represents a plausible combined exposure for many people, it is crucial to discuss how it might happen at a mechanistic level. SARS-CoV-2 causes the coronavirus disease 2019 (COVID-19), which can spread by contact and respiratory droplets. One may be exposed to lead and SARS-CoV-2 simultaneously depending on their living situation. Uncertainty exists regarding the consequences of this exposure on cardiovascular disease and its underlying processes. This paper aims to investigate these mechanisms and provide potential causes behind them. The interaction of lead with SARS-CoV-2 and its effects on the cardiovascular system are likely caused by direct damage, the promotion of reactive oxygen species (ROS) production, oxidative stress, and inflammation. In particular, the stimulation of nuclear factor kappa B (NF-B), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF) were all identified as potentially important pathways. Blood clotting, cytokine storm cycle development, and increased hypoxia may also be the results of this. In summary, the effects on the immune system, coagulation, the renin-angiotensin-aldosterone system, and cardiac contractility were the four primary areas identified.

Exposure to parabens and dysglycemia: Insights from a Chinese population.

BACKGROUND: Parabens, a widely exposed environmental endocrine disruptor, were reported to disturb glucose metabolism through various pathways in animal models, but epidemiologic studies are limited. Therefore, this study aimed to investigate the plasma parabens level in rural populations and their effects of single and mixed paraben exposure on T2DM based on the Henan Rural Cohort. METHODS: A total of 1713 participants (880 T2DM and 833 controls) from the Henan Rural Cohort Study were included in this case-control study. Generalized linear regression models were performed to assess the single and joint effects of parabens on T2DM and glucose metabolism indicators. In addition, the dose-response relationship of plasma parabens with T2DM and glucose metabolism indicators were explored by the restricted cubic splines. Bayesian kernel machine regression (BKMR) and quantile g-computation models were utilized to assess overall associations of paraben mixtures with T2DM and glucose metabolism indicators. RESULTS: Σparabens and methylparaben (MeP) exposure significantly increased the risk of T2DM (P < 0.01). However, ethylparaben (EtP) and butylparaben (BuP) were negatively related to T2DM (P < 0.01). Notably, non-linear relationships of EtP and BuP with T2DM were observed. When the level of EtP or BuP was above the inflection point observed in dose-response curve, the ORs and 95% CIs were 1.453 (1.252, 1.686) and 1.982 (1.444, 2.721), respectively. Moreover, the result of quantile g-computation also showed that exposure to high concentration of parabens mixture was positively related to the risk of T2DM. BKMR model indicated that parabens mixture was associated with glycometabolism following a U-shape and parabens mixture increased the risk of dysglycemia when all parabens concentrations were at or above their 55th percentile compared with the median. CONCLUSION: MeP or paraben mixture exposure levels showed a linear positive association with risk of T2DM. EtP and BuP were nonlinearly associated with glucose metabolism and moderate-high exposure contributed to T2DM.

Towards Whole Health Toxicology: In-Silico Prediction of Diseases Sensitive to Multi-Chemical Exposures.

Chemical exposures from diverse sources merge on a limited number of molecular pathways described as toxicity pathways. Changes in the same set of molecular pathways in different cell and tissue types may generate seemingly unrelated health conditions. Today, no approaches are available to predict in an unbiased way sensitivities of different disease states and their combinations to multi-chemical exposures across the exposome. We propose an inductive in-silico workflow where sensitivities of genes to chemical exposures are identified based on the overlap of existing genomic datasets, and data on sensitivities of individual genes is further used to sequentially derive predictions on sensitivities of molecular pathways, disease states, and groups of disease states (syndromes). Our analysis predicts that conditions representing the most significant public health problems are among the most sensitive to cumulative chemical exposures. These conditions include six leading types of cancer in the world (prostatic, breast, stomach, lung, colorectal neoplasms, and hepatocellular carcinoma), obesity, type 2 diabetes, non-alcoholic fatty liver disease, autistic disorder, Alzheimer's disease, hypertension, heart failure, brain and myocardial ischemia, and myocardial infarction. Overall, our predictions suggest that environmental risk factors may be underestimated for the most significant public health problems.

Elevated somatic mutation and evidence of genomic instability in veterans with Gulf War illness.

AIMS: Gulf War illness (GWI) is thought to be associated with exposures experienced by soldiers deployed in the 1991 Gulf War. A major question is how these exposures continue to influence the health of these individuals three decades later. One potentially permanent effect of such exposures is the induction of genetic mutations. We investigated whether veterans with GWI exhibited persistently elevated levels of somatic mutation. MATERIALS AND METHODS: We applied the blood-based glycophorin A (GPA) somatic mutation assay to a cohort of veterans diagnosed with GWI and a set of both concurrent and historic age-matched controls. This assay quantifies red blood cells with a phenotype consistent with loss of one allele at the genetic determinant for the MN blood group, the GPA gene. KEY FINDINGS: As a population, those affected with GWI exhibited an uninduced mutation frequency at the GPA locus that was effectively twice that observed in controls, a result that was statistically significant. This result was influenced by an increase in the incidence of individuals with aberrantly high mutation frequencies, seemingly higher than would be expected by dose extrapolation and consistent with the induction of localized genomic instability in the hematopoietic bone marrow stem cells. When these "outliers" were removed from consideration, the remaining affected population retained a significantly higher mean allele loss mutation frequency, suggesting that both dose-dependent bone marrow genotoxicity and induction of genomic instability are contributing to the elevation in mutation frequency in these affected veterans. SIGNIFICANCE: This study provides evidence that manifestation of GWI is associated with increased cumulative exposure to agents capable of inducing persistent mutations in bone marrow stem cells. Whether these mutations are involved in the clinical aspects of the condition or are simply biomarkers of overall exposure has yet to be determined. The increased incidence of genomic instability suggests that this persistent mutation can have important delayed effects on cellular integrity.

Combined Effect of Lead Exposure and Allostatic Load on Cardiovascular Disease Mortality-A Preliminary Study.

This study explores the combined effect of lead (Pb) exposure and an index of chronic physiological stress on cardiovascular disease mortality using data from the National Health and Nutrition Examination Survey (NHANES) 1999-2008 linked to 1999-2014 National Death Index data. Chronic physiological stress was measured using the allostatic load (AL) index, which was formed by analyzing markers from the cardiovascular, inflammatory, and metabolic systems, with Pb levels, assessed using blood lead levels (BLL). The dataset was analyzed with statistical techniques to explore (a) the relationship between Pb exposure and AL, and (b) the combined role of Pb and AL on cardiovascular disease mortality. Results indicated that AL was more elevated in those with BLLs above the 50th percentile in the US population and that those with elevated AL were more likely to have high BLL. Finally, the interaction of AL and BLL significantly increased the likelihood of cardiovascular disease mortality. These findings highlight the need for considering the totality of exposures experienced by populations to build holistic programs to prevent Pb exposure and reduce stressors to promote optimal health outcomes and reduce cardiovascular mortality risk.

Understanding mixed environmental exposures using metabolomics via a hierarchical community network model in a cohort of California women in 1960's.

Even though the majority of population studies in environmental health focus on a single factor, environmental exposure in the real world is a mixture of many chemicals. The concept of "exposome" leads to an intellectual framework of measuring many exposures in humans, and the emerging metabolomics technology offers a means to read out both the biological activity and environmental impact in the same dataset. How to integrate exposome and metabolome in data analysis is still challenging. Here, we employ a hierarchical community network to investigate the global associations between the metabolome and mixed exposures including DDTs, PFASs and PCBs, in a women cohort with sera collected in California in the 1960s. Strikingly, this analysis revealed that the metabolite communities associated with the exposures were non-specific and shared among exposures. This suggests that a small number of metabolic phenotypes may account for the response to a large class of environmental chemicals.

Environmental impact and risk assessments and key factors contributing to the overall uncertainties.

There is a significant number of nuclear and radiological sources that have contributed, are still contributing, or have the potential to contribute to radioactive contamination of the environment in the future. To protect the environment from radioactive contamination, impact and risk assessments are performed prior to or during a release event, short or long term after deposition or prior and after implementation of countermeasures. When environmental impact and risks are assessed, however, a series of factors will contribute to the overall uncertainties. To provide environmental impact and risk assessments, information on processes, kinetics and a series of input variables is needed. Adding problems such as variability, questionable assumptions, gaps in knowledge, extrapolations and poor conceptual model structures, a series of factors are contributing to large and often unacceptable uncertainties in impact and risk assessments. Information on the source term and the release scenario is an essential starting point in impact and risk models; the source determines activity concentrations and atom ratios of radionuclides released, while the release scenario determine the physico-chemical forms of released radionuclides such as particle size distribution, structure and density. Releases will most often contain other contaminants such as metals, and due to interactions, contaminated sites should be assessed as a multiple stressor scenario. Following deposition, a series of stressors, interactions and processes will influence the ecosystem transfer of radionuclide species and thereby influence biological uptake (toxicokinetics) and responses (toxicodynamics) in exposed organisms. Due to the variety of biological species, extrapolation is frequently needed to fill gaps in knowledge e.g., from effects to no effects, from effects in one organism to others, from one stressor to mixtures. Most toxtests are, however, performed as short term exposure of adult organisms, ignoring sensitive history life stages of organisms and transgenerational effects. To link sources, ecosystem transfer and biological effects to future impact and risks, a series of models are usually interfaced, while uncertainty estimates are seldom given. The model predictions are, however, only valid within the boundaries of the overall uncertainties. Furthermore, the model predictions are only useful and relevant when uncertainties are estimated, communicated and understood. Among key factors contributing most to uncertainties, the present paper focuses especially on structure uncertainties (model bias or discrepancies) as aspects such as particle releases, ecosystem dynamics, mixed exposure, sensitive life history stages and transgenerational effects, are usually ignored in assessment models. Research focus on these aspects should significantly reduce the overall uncertainties in the impact and risk assessment of radioactive contaminated ecosystems.

Toxicity mechanisms of ionic silver and polymer-coated silver nanoparticles with interactions of functionalized carbon nanotubes on early development stages of sea urchin.

Exposures of aquatic organisms to multiple contaminants are likely to take place in estuarine and coastal areas and combined effects on early life stages have to be examined. Among emerging contaminants, ionic silver (Ag(+)) and silver nanoparticles (AgNps) have demonstrated contrasting effects on marine invertebrates, but their interactions with functionalized carbon nanotubes (f-SWCNTs) have not yet been investigated in details. In order to observe the impacts and understand the toxicity mechanism of Ag(+) and polymer-coated AgNps, and their combined effects with f-SWCNTs, successive development stages of embryos of sea urchin, Strongylocentrotus droebachiensis, were exposed to Ag(+), AgNps and f-SWCNTs, separately and in mixtures using moderate environmental concentrations. We also assessed long-term effects of treatments under recovery conditions. Morphological endpoints such as archenteron elongation, primary and secondary mesenchyme cells fate, pigment cells migration, spiculogenic cells and gut development indicated different effects of silver and nanosilver forms during successive development stages. Whereas Ag(+) induced vegetalization and extrusion of mesenchyme cells on early embryos; f-SWCNTs+Ag(+) strongly interfered with gut regionalization in late larvae. Sensitive blastocoelar cells got vacuolized and shapeless with AgNps, but not with mixtures with f-SWCNTs. Increased concentrations of Ag(+) and f-SWCNTs+Ag(+) led to the most disruptive effects during development, but f-SWCNTs+Ag(+) caused the highest mortality rate during the recovery period, which indicated far-reaching effects driven by f-SWCNTs and their ability to keep silver more available during exposure period.