Geographic clustering of elevated blood heavy metal levels in pregnant women.

BACKGROUND: Cadmium (Cd), lead (Pb), mercury (Hg), and arsenic (As) exposure is ubiquitous and has been associated with higher risk of growth restriction and cardiometabolic and neurodevelopmental disorders. However, cost-efficient strategies to identify at-risk populations and potential sources of exposure to inform mitigation efforts are limited. The objective of this study was to describe the spatial distribution and identify factors associated with Cd, Pb, Hg, and As concentrations in peripheral blood of pregnant women. METHODS: Heavy metals were measured in whole peripheral blood of 310 pregnant women obtained at gestational age ~12 weeks. Prenatal residential addresses were geocoded and geospatial analysis (Getis-Ord Gi* statistics) was used to determine if elevated blood concentrations were geographically clustered. Logistic regression models were used to identify factors associated with elevated blood metal levels and cluster membership. RESULTS: Geospatial clusters for Cd and Pb were identified with high confidence (p-value for Gi* statistic <0.01). The Cd and Pb clusters comprised 10.5 and 9.2 % of Durham County residents, respectively. Medians and interquartile ranges of blood concentrations (µg/dL) for all participants were Cd 0.02 (0.01-0.04), Hg 0.03 (0.01-0.07), Pb 0.34 (0.16-0.83), and As 0.04 (0.04-0.05). In the Cd cluster, medians and interquartile ranges of blood concentrations (µg/dL) were Cd 0.06 (0.02-0.16), Hg 0.02 (0.00-0.05), Pb 0.54 (0.23-1.23), and As 0.05 (0.04-0.05). In the Pb cluster, medians and interquartile ranges of blood concentrations (µg/dL) were Cd 0.03 (0.02-0.15), Hg 0.01 (0.01-0.05), Pb 0.39 (0.24-0.74), and As 0.04 (0.04-0.05). Co-exposure with Pb and Cd was also clustered, the p-values for the Gi* statistic for Pb and Cd was <0.01. Cluster membership was associated with lower education levels and higher pre-pregnancy BMI. CONCLUSIONS: Our data support that elevated blood concentrations of Cd and Pb are spatially clustered in this urban environment compared to the surrounding areas. Spatial analysis of metals concentrations in peripheral blood or urine obtained routinely during prenatal care can be useful in surveillance of heavy metal exposure.

Modeling approaches for characterizing and evaluating environmental exposure to engineered nanomaterials in support of risk-based decision making.

As the use of engineered nanomaterials becomes more prevalent, the likelihood of unintended exposure to these materials also increases. Given the current scarcity of experimental data regarding fate, transport, and bioavailability, determining potential environmental exposure to these materials requires an in depth analysis of modeling techniques that can be used in both the near- and long-term. Here, we provide a critical review of traditional and emerging exposure modeling approaches to highlight the challenges that scientists and decision-makers face when developing environmental exposure and risk assessments for nanomaterials. We find that accounting for nanospecific properties, overcoming data gaps, realizing model limitations, and handling uncertainty are key to developing informative and reliable environmental exposure and risk assessments for engineered nanomaterials. We find methods suited to recognizing and addressing significant uncertainty to be most appropriate for near-term environmental exposure modeling, given the current state of information and the current insufficiency of established deterministic models to address environmental exposure to engineered nanomaterials.

Using river distance and existing hydrography data can improve the geostatistical estimation of fish tissue mercury at unsampled locations.

Mercury in fish tissue is a major human health concern. Consumption of mercury-contaminated fish poses risks to the general population, including potentially serious developmental defects and neurological damage in young children. Therefore, it is important to accurately identify areas that have the potential for high levels of bioaccumulated mercury. However, due to time and resource constraints, it is difficult to adequately assess fish tissue mercury on a basin wide scale. We hypothesized that, given the nature of fish movement along streams, an analytical approach that takes into account distance traveled along these streams would improve the estimation accuracy for fish tissue mercury in unsampled streams. Therefore, we used a river-based Bayesian Maximum Entropy framework (river-BME) for modern space/time geostatistics to estimate fish tissue mercury at unsampled locations in the Cape Fear and Lumber Basins in eastern North Carolina. We also compared the space/time geostatistical estimation using river-BME to the more traditional Euclidean-based BME approach, with and without the inclusion of a secondary variable. Results showed that this river-based approach reduced the estimation error of fish tissue mercury by more than 13% and that the median estimate of fish tissue mercury exceeded the EPA action level of 0.3 ppm in more than 90% of river miles for the study domain.

Using river distances in the space/time estimation of dissolved oxygen along two impaired river networks in New Jersey.

Understanding surface water quality is a critical step towards protecting human health and ecological stability. Because of resource deficiencies and the large number of river miles needing assessment, there is a need for a methodology that can accurately depict river water quality where data do not exist. The objective of this research is to implement a methodology that incorporates a river metric into the space/time analysis of dissolved oxygen data for two impaired river basins. An efficient algorithm is developed to calculate river distances within the BMElib statistical package for space/time geostatistics. We find that using a river distance in a space/time context leads to an appreciable 10% reduction in the overall estimation error, and results in maps of DO that are more realistic than those obtained using a Euclidean distance. As a result river distance is used in the subsequent non-attainment assessment of DO for two impaired river basins in New Jersey.

Cadmium exposure and MEG3 methylation differences between Whites and African Americans in the NEST Cohort.

Cadmium (Cd) is a ubiquitous environmental pollutant associated with a wide range of health outcomes including cancer. However, obscure exposure sources often hinder prevention efforts. Further, although epigenetic mechanisms are suspected to link these associations, gene sequence regions targeted by Cd are unclear. Aberrant methylation of a differentially methylated region (DMR) on the MEG3 gene that regulates the expression of a cluster of genes including MEG3, DLK1, MEG8, MEG9 and DIO3 has been associated with multiple cancers. In 287 infant-mother pairs, we used a combination of linear regression and the Getis-Ord Gi* statistic to determine if maternal blood Cd concentrations were associated with offspring CpG methylation of the sequence region regulating a cluster of imprinted genes including MEG3. Correlations were used to examine potential sources and routes. We observed a significant geographic co-clustering of elevated prenatal Cd levels and MEG3 DMR hypermethylation in cord blood (P = 0.01), and these findings were substantiated in our statistical models (ß = 1.70, se = 0.80, P = 0.03). These associations were strongest in those born to African American women (ß = 3.52, se = 1.32, P = 0.01) compared with those born to White women (ß = 1.24, se = 2.11, P = 0.56) or Hispanic women (ß = 1.18, se = 1.24, P = 0.34). Consistent with Cd bioaccumulation during the life course, blood Cd levels increased with age (ß = 0.015 µg/dl/year, P = 0.003), and Cd concentrations were significantly correlated between blood and urine (ρ > 0.47, P < 0.01), but not hand wipe, soil or house dust concentrations (P > 0.05). Together, these data support that prenatal Cd exposure is associated with aberrant methylation of the imprint regulatory element for the MEG3 gene cluster at birth. However, neither house-dust nor water are likely exposure sources, and ingestion via contaminated hands is also unlikely to be a significant exposure route in this population. Larger studies are required to identify routes and sources of exposure.

Validation and sensitivity of the FINE Bayesian network for forecasting aquatic exposure to nano-silver.

The adaptive nature of the Forecasting the Impacts of Nanomaterials in the Environment (FINE) Bayesian network is explored. We create an updated FINE model (FINEAgNP-2) for predicting aquatic exposure concentrations of silver nanoparticles (AgNP) by combining the expert-based parameters from the baseline model established in previous work with literature data related to particle behavior, exposure, and nano-ecotoxicology via parameter learning. We validate the AgNP forecast from the updated model using mesocosm-scale field data and determine the sensitivity of several key variables to changes in environmental conditions, particle characteristics, and particle fate. Results show that the prediction accuracy of the FINEAgNP-2 model increased approximately 70% over the baseline model, with an error rate of only 20%, suggesting that FINE is a reliable tool to predict aquatic concentrations of nano-silver. Sensitivity analysis suggests that fractal dimension, particle diameter, conductivity, time, and particle fate have the most influence on aquatic exposure given the current knowledge; however, numerous knowledge gaps can be identified to suggest further research efforts that will reduce the uncertainty in subsequent exposure and risk forecasts.

A web-based tool to engage stakeholders in informing research planning for future decisions on emerging materials.

Prioritizing and assessing risks associated with chemicals, industrial materials, or emerging technologies is a complex problem that benefits from the involvement of multiple stakeholder groups. For example, in the case of engineered nanomaterials (ENMs), scientific uncertainties exist that hamper environmental, health, and safety (EHS) assessments. Therefore, alternative approaches to standard EHS assessment methods have gained increased attention. The objective of this paper is to describe the application of a web-based, interactive decision support tool developed by the U.S. Environmental Protection Agency (U.S. EPA) in a pilot study on ENMs. The piloted tool implements U.S. EPA's comprehensive environmental assessment (CEA) approach to prioritize research gaps. When pursued, such research priorities can result in data that subsequently improve the scientific robustness of risk assessments and inform future risk management decisions. Pilot results suggest that the tool was useful in facilitating multi-stakeholder prioritization of research gaps. Results also provide potential improvements for subsequent applications. The outcomes of future CEAWeb applications with larger stakeholder groups may inform the development of funding opportunities for emerging materials across the scientific community (e.g., National Science Foundation Science to Achieve Results [STAR] grants, National Institutes of Health Requests for Proposals).

The use of Bayesian networks for nanoparticle risk forecasting: model formulation and baseline evaluation.

We describe the use of Bayesian networks as a tool for nanomaterial risk forecasting and develop a baseline probabilistic model that incorporates nanoparticle specific characteristics and environmental parameters, along with elements of exposure potential, hazard, and risk related to nanomaterials. The baseline model, FINE (Forecasting the Impacts of Nanomaterials in the Environment), was developed using expert elicitation techniques. The Bayesian nature of FINE allows for updating as new data become available, a critical feature for forecasting risk in the context of nanomaterials. The specific case of silver nanoparticles (AgNPs) in aquatic environments is presented here (FINE(AgNP)). The results of this study show that Bayesian networks provide a robust method for formally incorporating expert judgments into a probabilistic measure of exposure and risk to nanoparticles, particularly when other knowledge bases may be lacking. The model is easily adapted and updated as additional experimental data and other information on nanoparticle behavior in the environment become available. The baseline model suggests that, within the bounds of uncertainty as currently quantified, nanosilver may pose the greatest potential risk as these particles accumulate in aquatic sediments.

Modern space/time geostatistics using river distances: data integration of turbidity and E. coli measurements to assess fecal contamination along the Raritan River in New Jersey.

Escherichia coli (E. coli) is a widely used indicator of fecal contamination in water bodies. External contact and subsequent ingestion of bacteria coming from fecal contamination can lead to harmful health effects. Since E. coli data are sometimes limited, the objective of this study is to use secondary information in the form of turbidity to improve the assessment of E. coli at unmonitored locations. We obtained all E. coli and turbidity monitoring data available from existing monitoring networks for the 2000-2006 time period for the Raritan River Basin, New Jersey. Using collocated measurements, we developed a predictive model of E. coli from turbidity data. Using this model, soft data are constructed for E. coli given turbidity measurements at 739 space/time locations where only turbidity was measured. Finally, the Bayesian Maximum Entropy (BME) method of modern space/time geostatistics was used for the data integration of monitored and predicted E. coli data to produce maps showing E. coli concentration estimated daily across the river basin. The addition of soft data in conjunction with the use of river distances reduced estimation error by about 30%. Furthermore, based on these maps, up to 35% of river miles in the Raritan Basin had a probability of E coli impairment greater than 90% on the most polluted day of the study period.

Space/time analysis of fecal pollution and rainfall in an eastern North Carolina estuary.

The Newport River Estuary (NPRE) is a high-priority shellfish harvesting area in eastern North Carolina that is impaired due to fecal contamination, specifically exceeding recommended levels for fecal coliforms. A hydrologic-driven mean trend model was developed, as a function of antecedent rainfall, in the NPRE to predict levels of Escherichia coli (EC, measured as a proxyforfecal coliforms). This mean trend model was integrated in a Bayesian Maximum Entropy (BME) framework to produce informative space/time (S/T) maps depicting fecal contamination across the NPRE during winter and summer months. These maps showed that during dry winter months, corretponding to the oyster harvesting season in North Carolina (October 1-March 30), predicted EC concentrations were below the shellfish harvesting standard (14 MPN/100 mL). However, after substantial rainfall of 3.81 cm (1.5 in.), the NPRE did not appear to mee this requirement. Warmer months resulted in the predicted EC concentrations exceeding the threshold for the NPRE. Predicted ENT concentrations were generally below the recreational water quality threshold (104 MPN/100 mL), except for warmer months after substantial rainfall. Once established, this combined approach produces near real-time visual information on which to base water quality management decisions.

Rhesus monkey rhadinovirus (RRV): construction of a RRV-GFP recombinant virus and development of assays to assess viral replication.

Rhesus monkey rhadinovirus (RRV) is a gamma-2-herpesvirus that is closely related to Kaposi's sarcoma-associated herpesvirus (KSHV/HHV-8). Lack of an efficient culture system to grow high titers of virus, and the lack of an in vivo animal model system, has hampered the study of KSHV replication and pathogenesis. RRV is capable of replicating to high titers on fibroblasts, thus facilitating the construction of recombinant rhadinoviruses. In addition, the ability to experimentally infect naïve rhesus macaques with RRV makes it an excellent model system to study gamma-herpesvirus replication. Our study describes, for the first time, the construction of a GFP-expressing RRV recombinant virus using a traditional homologous recombination strategy. We have also developed two new methods for determining viral titers of RRV including a traditional viral plaque assay and a quantitative real-time PCR assay. We have compared the replication of wild-type RRV with that of the RRV-GFP recombinant virus in one-step growth curves. We have also measured the sensitivity of RRV to a small panel of antiviral drugs. The development of both the recombination strategy and the viral quantitation assays for RRV will lay the foundation for future studies to evaluate the contribution of individual genes to viral replication both in vitro and in vivo.