Benchmark dose modeling for epidemiological dose-response assessment using prospective cohort studies.

Benchmark dose (BMD) methodology has been employed as a default dose-response modeling approach to determine the toxicity value of chemicals to support regulatory chemical risk assessment. Especially, a relatively standardized BMD analysis framework has been established for modeling toxicological data regarding the formats of input data, dose-response models, definitions of benchmark response, and model uncertainty consideration. However, the BMD approach has not been well developed for epidemiological data mainly because of the diverse designs of epidemiological studies and various formats of data reported in the literature. Although most of the epidemiological BMD analyses were developed to solve a particular question, the methods proposed in two recent studies are able to handle cohort and case-control studies using summary data with consideration of adjustments for confounders. Therefore, the purpose of the present study is to investigate and compare the "effective count"-based BMD modeling approach and adjusted relative risk (RR)-based BMD analysis approach to identify an appropriate BMD modeling framework that can be generalized for analyzing published data of prospective cohort studies for BMD analysis. The two methods were applied to the same set of studies that investigated the association between bladder and lung cancer and inorganic arsenic exposure for BMD estimation. The results suggest that estimated BMDs and BMDLs are relatively consistent; however, with the consideration of established common practice in BMD analysis, modeling adjusted RR values as continuous data for BMD estimation is a more generalizable approach harmonized with the BMD approach using toxicological data.

The Effect of Historical Data-Based Informative Prior on Benchmark Dose Estimation of Toxicogenomics.

High-throughput toxicogenomics as an advanced toolbox of Tox21 plays an increasingly important role in facilitating the toxicity assessment of environmental chemicals. However, toxicogenomic dose-response analyses are typically challenged by limited data, which may result in significant uncertainties in parameter and benchmark dose (BMD) estimation. Integrating historical data via prior distribution using a Bayesian method is a useful but not-well-studied strategy. The objective of this study is to evaluate the effectiveness of informative priors in genomic dose-response modeling and BMD estimation. Specifically, we aim to identify plausible informative priors and evaluate their effects on BMD estimates at both gene and pathway levels. A general informative prior and eight time-specific (from 3 h to 29 d) informative priors for seven commonly used continuous dose-response models were derived. Results suggest that the derived informative priors are sensitive to the specific data sets used for elicitation. Real data-based simulations indicate that BMD estimation with the time-specific informative priors can achieve increased or equivalent accuracy, significantly decreased uncertainty, and a slightly enhanced correlation with the points of departure estimated from apical end points than the counterparts with noninformative priors. Overall, our study systematically examined the effects of historical data-based informative priors on BMD estimates, highlighting the benefits of plausible information priors in advancing the practice of toxicogenomics.

Margin of exposure to free formaldehyde in personal care products containing formaldehyde-donor preservatives: Evidence for consumer safety.

Formaldehyde has been classified as carcinogenic to humans by International Agency for Research on Cancer and found in personal care (PC) products containing formaldehyde-donor (FD) preservatives. However, the cancer risk associated with the use of FD-containing PC products has not been well established. Our study provides the quantitative cancer risk assessment of formaldehyde in FD-containing PC products. The carbon-13 nuclear magnetic resonance (13C-NMR) spectroscopy was used in this risk assessment to provide reliable exposure information to formaldehyde in PC products and aqueous solutions containing sodium hydroxymethylglycinate. The risk assessment was conducted using the margin of exposure (MOE) approach with benchmark doses (BMDs) for 10% effect. For hemolymphoreticular neoplasias in male rats, a BMD of 28.03 mg/kg/day and a BMD lower confidence limit (BMDL) of 2.52 mg/kg/day were calculated from available long-term animal experiments. The worst-case consumer exposure to formaldehyde from FD-containing PC products was 0.007 µg/kg/day. Comparing the consumer exposure with BMDL, the resulting MOE was 360,000 for the worst-case scenario. The consumer exposure to formaldehyde (0.007 µg/kg/day) from using FD-containing PC products represents less than 1.0 × 10-6 % of background level endogenous formaldehyde (878-1310 mg/kg/day). The cancer risk from formaldehyde to consumers using FD-containing PC products is negligible.

Circular RNA circEIF4G2 aggravates renal fibrosis in diabetic nephropathy by sponging miR-218.

Circular RNAs play essential roles in the development of various human diseases. However, how circRNAs are involved in diabetic nephropathy (DN) are not fully understood. Our study aimed to investigate the effects of circRNA circEIF4G2 on DN. Experiments were performed in the db/db mouse model of type 2 diabetes and NRK-52E cells. We found that circEIF4G2 was significantly up-regulated in the kidneys of db/db mice and NRK-52E cells stimulated by high glucose. circEIF4G2 knockdown inhibited the expressions of TGF-ß1, Collagen I and Fibronectin in high glucose-stimulated NRK-52E cells, which could be rescued by miR-218 inhibitor. Knockdown of SERBP1 reduced the expression of TGF-ß1, Collagen I and Fibronectin in HG-stimulated NRK-52E cells. In summary, our findings suggested that circEIF4G2 promotes renal tubular epithelial cell fibrosis via the miR-218/SERBP1 pathway, presenting a novel insight for DN treatment.

Using Prior Toxicological Data to Support Dose-Response Assessment─Identifying Plausible Prior Distributions for Dichotomous Dose-Response Models.

The benchmark dose (BMD) methodology has significantly advanced the practice of dose-response analysis and created substantial opportunities to enhance the plausibility of BMD estimation by synthesizing dose-response information from different sources. Particularly, integrating existing toxicological information via prior distribution in a Bayesian framework is a promising but not well-studied strategy. The study objective is to identify a plausible way to incorporate toxicological information through informative prior to support BMD estimation using dichotomous data. There are four steps in this study: determine appropriate types of distribution for parameters in common dose-response models, estimate the parameters of the determined distributions, investigate the impact of alternative strategies of prior implementation, and derive endpoint-specific priors to examine how prior-eliciting data affect priors and BMD estimates. A plausible distribution was estimated for each parameter in the common dichotomous dose-response models using a general database. Alternative strategies for implementing informative prior have a limited impact on BMD estimation, but using informative prior can significantly reduce uncertainty in BMD estimation. Endpoint-specific informative priors are substantially different from the general one, highlighting the necessity for guidance on prior elicitation. The study developed a practical way to employ informative prior and laid a foundation for advanced Bayesian BMD modeling.

Health risk assessment of As due to rice ingestion based on iAs distribution and actual consumption patterns for the residents in Beijing: a cross-sectional study.

As a well-known human carcinogen, arsenic (As) could pose various detrimental health effects to humans mainly through the exposure pathway of food ingestion. In comparison with other foods, rice can accumulate more arsenic due to its tissue specificity. Thus, it is of great significance to assess the health risk of As due to rice ingestion. However, the study on risk assessment from exposure to As in rice is still in an early stage and lack accuracy to date. In this study, after obtaining the rice exposure behavior patterns based on a questionnaire survey, a total of 160 rice samples, which consisted of 4 types (i.e., japonica, indica, glutinous and brown rice), rice from 4 areas and consumed by most of the population in Beijing, were collected. On the basis of the actual intake rate and the species weighted average concentration of consumed rice, average daily exposure dose and health risks of inorganic As (iAs) from rice ingestion were assessed for the population among different genders and ages in Beijing. The results show that japonica rice and rice from Northeast China had higher As content, with the same value of 0.064 mg kg-1. And, they were the most popular rice consumed by people, with the intake rates of 75.50 g d-1, and 67.91 g d-1, respectively. The proportion of iAs to total As (tAs) was 58.34%, with a range of 43.18-71.88%. The average daily dose of iAs for the population was 1.15 × 10-4, which mainly came from japonica rice and the rice from Northeast China ingestion. In comparison with the acceptable non-cancer risk, which had a HQ value of 0.38, the carcinogenic risk of the population in Beijing was 1.73 × 10-4 on average. Furthermore, males had higher carcinogenic risk (1.88 × 10-4) than females (1.62 × 10-4), and the people in the age of 45-55 suffered from the highest carcinogenic risk (2.22 × 10-4), which mainly was attributed to the japonica rice and the rice from Northeast China. This study strengthened that appropriate dietary patterns should be paid more attention in order to control the health risk due to As exposure.

MicroRNA-139-5p Alleviates High Glucose-Triggered Human Retinal Pigment Epithelial Cell Injury by Targeting LIM-Only Factor 4.

Diabetic retinopathy (DR) is a type of diabetes complication, which can result in loss of vision in adults worldwide. Increasing evidence has revealed that microRNAs (miRs) can regulate DR progression. Thus, the present study was aimed at assessing the possible mechanism of miR-139-5p in high glucose- (HG-) incubated retinal pigment epithelial (ARPE-19) cells. The present results demonstrated that miR-139-5p expression was notably reduced in the serum samples of patients with DR, as well as in ARPE-19 cells treated with HG in a time-dependent manner. Moreover, miR-139-5p was markedly overexpressed by transfection of miR-139-5p mimics into ARPE-19 cells. Overexpression of miR-139-5p markedly induced cell viability and repressed HG-triggered apoptosis. Furthermore, overexpression of miR-139-5p relived HG-enhanced oxidative stress injury. It was found that HG induced malondialdehyde levels but decreased superoxide dismutase and glutathione peroxidase activities in ARPE-19 cells. In addition, overexpression of miR-139-5p could markedly decrease intracellular stress. The results demonstrated that overexpression of miR-139-5p effectively repressed HG-activated inflammation, as indicated by the upregulation of inflammation cytokines, including TNF-α, IL-6, and Cox-2, in ARPE-19 cells. Subsequently, it was identified that LIM-only factor 4 (LMO4) could act as a downstream target for miR-139-5p. LMO4 expression was significantly increased in patients with DR and HG-treated ARPE-19 cells. Mechanistically, knockdown of LMO4 reversed the biological role of miR-139-5p in proliferation, apoptosis, oxidative stress, and release of inflammation factors in vitro. Collectively, these results suggested that miR-139-5p significantly decreased ARPE-19 cell injury caused by HG by inducing proliferation and suppressing cell apoptosis, oxidant stress, and inflammation by modulating LMO4.

LncRNA SNHG16 induces proliferation and fibrogenesis via modulating miR-141-3p and CCND1 in diabetic nephropathy.

LncRNAs are reported to participate in the progression of various diseases including diabetic nephropathy. Currently, we reported that SNHG16 was obviously upregulated in db/db mice and high glucose-treated mice mesangial cells. Then, functional experiments showed that SNHG16 silencing significantly inhibited proliferation of mice mesangial cells, which induced the apoptosis and triggered cell cycle arrest. Meanwhile, proliferation-related biomarkers PCNA and Cyclin D1 (CCND1) were greatly repressed. Furthermore, western blot analysis was conducted to test fibrogenesis-associated genes Fibronectin and α-SMA. Meanwhile, the increased protein expression levels of Fibronectin and α-SMA under high glucose conditions were reversed by loss of SNHG16. miR-141-3p has been reported to be involved in various diseases. Then, RNA immunoprecipitation assay revealed the relation between SNHG16 and miR-141-3p. Downregulation of SNHG16 was able to induce expression of miR-141-3p, which was obviously reduced in db/db diabetic nephropathy mice. In addition, CCND1 is a crucial cell cycle master in human diseases. CCND1 was speculated as the target of miR-141-3p and miR-141-3p inhibited CCND1 expression significantly. Meanwhile, we observed that loss of CCND1 greatly repressed mice mesangial cell proliferation and induced cell apoptosis. Taken these together, we revealed for the first time that SNHG16 induced proliferation and fibrogenesis via modulating miR-141-3p and CCND1 in diabetic nephropathy. SNHG16/miR-141-3p/CCND1 axis can suggest a pathological mechanism of progression of diabetic nephropathy.

Knockdown of NEAT1 exerts suppressive effects on diabetic retinopathy progression via inactivating TGF-ß1 and VEGF signaling pathways.

Diabetic retinopathy (DR) is complication resulted from Type 2 diabetes mellitus. Accumulating evidence has proved the functions of long noncoding RNAs (lncRNAs) in the progression of DR. Recent reports exert the numerous regulatory functions of lncRNA nuclear-enriched abundant transcript 1 (NEAT1) in various diseases. However, its implications in DR remain barely known. Therefore, this study was carried out to explore the role of NEAT1 in high-glucose (HG)-triggered injury of human retinal endothelial cells (hRECs). Here, we found the NEAT1 level was significantly elevated in patients with DR, in the retina of diabetic rats and mice. Meanwhile, hRECs under HG stimuli also exhibited an increase of NEAT1. Moreover, the loss of NEAT1 enhanced hRECs proliferation and repressed HG-induced apoptosis, which was accompanied by an upregulation of Bcl-2 and a downregulation of Bax. Subsequently, the knockdown of NEAT1 obviously reduced HG-triggered oxidative stress injury in hRECs. It was reflected that intracellular reactive oxygen species and malondialdehyde level induced by HG were repressed by NEAT1 downregulation, while superoxide dismutase activity was increased. In addition, decreased NEAT1 repressed the inflammatory processes effectively as indicated by the inactivation of inflammatory cytokines Cox-2, interleukin-6, and tumor necrosis factor-α. Furthermore, vascular endothelial growth factor A (VEGF) and transforming growth factor-ß1 (TGF-ß1) expression in patients with DR, DR rats, and HG-incubated hRECs was obviously increased. The silence of NEAT1 could reduce the enhanced expression of VEGF and TGF-ß1 induced by HG. Hence, we concluded NEAT1 might contribute to the development of DR through activating TGF-ß1 and VEGF.

Assessment of the Dose-Response Relationship Between Folate Exposure and Cognitive Impairment: Synthesizing Data from Documented Studies.

The dose-response relationship between folate levels and cognitive impairment among individuals with vitamin B12 deficiency is an essential component of a risk-benefit analysis approach to regulatory and policy recommendations regarding folic acid fortification. Epidemiological studies provide data that are potentially useful for addressing this research question, but the lack of analysis and reporting of data in a manner suitable for dose-response purposes hinders the application of the traditional evidence synthesis process. This study aimed to estimate a quantitative dose-response relationship between folate exposure and the risk of cognitive impairment among older adults with vitamin B12 deficiency using "probabilistic meta-analysis," a novel approach for synthesizing data from observational studies. Second-order multistage regression was identified as the best-fit model for the association between the probability of cognitive impairment and serum folate levels based on data generated by randomly sampling probabilistic distributions with parameters estimated based on summarized information reported in relevant publications. The findings indicate a "J-shape" effect of serum folate levels on the occurrence of cognitive impairment. In particular, an excessive level of folate exposure is predicted to be associated with a higher risk of cognitive impairment, albeit with greater uncertainty than the association between low folate exposure and cognitive impairment. This study directly contributes to the development of a practical solution to synthesize observational evidence for dose-response assessment purposes, which will help strengthen future nutritional risk assessments for the purpose of informing decisions on nutrient fortification in food.

Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose-Response Uncertainty.

Model averaging for dichotomous dose-response estimation is preferred to estimate the benchmark dose (BMD) from a single model, but challenges remain regarding implementing these methods for general analyses before model averaging is feasible to use in many risk assessment applications, and there is little work on Bayesian methods that include informative prior information for both the models and the parameters of the constituent models. This article introduces a novel approach that addresses many of the challenges seen while providing a fully Bayesian framework. Furthermore, in contrast to methods that use Monte Carlo Markov Chain, we approximate the posterior density using maximum a posteriori estimation. The approximation allows for an accurate and reproducible estimate while maintaining the speed of maximum likelihood, which is crucial in many applications such as processing massive high throughput data sets. We assess this method by applying it to empirical laboratory dose-response data and measuring the coverage of confidence limits for the BMD. We compare the coverage of this method to that of other approaches using the same set of models. Through the simulation study, the method is shown to be markedly superior to the traditional approach of selecting a single preferred model (e.g., from the U.S. EPA BMD software) for the analysis of dichotomous data and is comparable or superior to the other approaches.

Metals source apportionment in farmland soil and the prediction of metal transfer in the soil-rice-human chain.

China is facing serious heavy metal pollution in farmland soil, which is a major pressing concern for food safety and human health. This research delivers an integrated methodology for pollution source apportionment and a soil-rice-human model to predict heavy metal transfer in the farmland soil, rice grain and human blood chain. The source identification integrated positive matrix factorization (PMF), cluster analysis (CA) and the life cycle assessment (LCA) survey of agricultural and industrial production and consumption. Based on the case analysis of Shaoxing, this method showed very good performance through the illustration of the source contributions by PMF and LCA at county level and the identification of the pollution sources using CA and LCA at field scale. According to the overall evaluation, the integrated method was superior for the farmland metals pollution source identification comparing to existing source apportionment methods. To predict metal transformation in soil-rice-human chain, a set of models of metals (As, Pb, Cd, Hg, Cr) accumulation ability in rice grain and human blood has been established by literature review and monitoring data. The models showed adequate predictability for the metal content of rice grains at both the field and regional scale, and plausible simulation of the metal concentration in human blood throughout the whole study region. Therefore, this study provides valuable tools for farmland soil heavy metal pollution source identification and for the prediction of heavy metal transformation in soil-rice-human chain; and it can highlight the need to take mitigating action to reduce farmland metal pollution risks in specific regions.

Health risks and source identification of dietary exposure to indicator polychlorinated biphenyls (PCBs) in Lanzhou, China.

Polychlorinated biphenyls (PCBs) are widely present in multiple environmental media even long after the phaseout, posing a health risk to the general population. Dietary intake is the major exposure route of PCBs; however, information is limited regarding PCBs in food that people directly consume. This study aims to measure personal exposure to indicator PCBs, evaluate the health risks, and identify their sources in a typical metropolitan city in China. Multi-day food samples were collected from 21 subjects in Lanzhou, Gansu Province, in two seasons using the duplicate plate method. Samples were extracted and analyzed for seven indicator PCBs using gas chromatography/mass spectrometry. Average daily doses (ADDs) of ∑7PCBs were estimated using Monte Carlo analysis with food intake information. Results show that PCB-118 and PCB-180 were the major congeners in food samples with average concentrations of 1.42 and 1.11 ng/g, respectively. The average (± SD) ADD of ∑7PCBs was 26.47 ± 22.10 ng/kg day among adults aged 18-69 years and displayed small variation across age groups. Comparing with the chronic RfD of 7 ng/kg day, 67% of people had their ADDs exceeding this threshold. The median cancer risk was 5.52 × 10-5, and 51% of residents had risks exceeding the action level of 10-4. The principal component analysis identified waste incineration, gasoline engine production, and leakage of #1 PCBs as the major PCBs sources. In conclusion, a large portion of Lanzhou residents has high non-cancer and cancer risks from dietary exposure to PCBs, which warrants control actions targeting these major sources.

Systematic-analysis of mRNA expression profiles in skeletal muscle of patients with type II diabetes: The glucocorticoid was central in pathogenesis.

Since the past 30 years, the prevalence of diabetes has more than doubled, making it an urgent challenge globally. We carried out systematic analysis with the public data of mRNA expression profiles in skeletal muscle to study the pathogenesis, since insulin resistance in the skeletal muscle is an early feature. We utilized three GEO datasets, containing total 60 cases and 63 normal samples. After the background removal, R package QC was utilized to finish the preprocessing of datasets. We obtained a dataset containing 2481 genes and 123 samples after the preprocessing. Quantitative quality control measures were calculated to represent the quality of these datasets. MetaDE package provides functions for conducting different systematic analysis methods for differential expression analysis. The GO term enrichment was carried out using PANTHER. Protein-protein interactions, drug-gene interactions, and genetic association of the identified differentially expressed genes were analyzed using STRING v10.0 online tool, DGIdb, and the Genetic Association Database, respectively. The datasets had good performances on IQC and EQC, which suggested that the datasets had good internal and external quality. Totally 96 differentially expressed genes were detected using 0.01 as cutoff of AW. The enriched GO terms were mainly associated with the response to glucocorticoid. There were seven genes involving in the gluconeogenesis were differentially expressed, which might be the potential treatment target for this disease. The closely connected networks and potential targets of existed drugs suggested that some of the drugs might be applied to the treatment of diabetes as well.

The key genes underlying pathophysiology association between the type 2-diabetic and colorectal cancer.

Although diabetes mellitus (DM) is reported as an independent risk factor for colorectal cancer (CRC) in many researches, the underlying pathophysiology is still unclear. We investigated the differentially expressed genes (DEGs) for the diabetes and CRC to reveal the underlying pathophysiological association between the type 2-diabetic (T2D) and CRC. Gene expression profiles for T2D (GSE55650), CRC (GSE8671), and Metformin treated cell lines (GSE67342) were downloaded from GEO database. The DEGs between T2D samples and their control samples were identified with t-test and variance analysis. After cluster analysis and functional enrichment analysis, protein-protein interaction (PPI) network was constructed to find potential genes for diabetes and CRC in Metformin's treatment. Totally, we identified 583 overlapped genes, 169 common DEGs, and 414 independent DEGs between T2D and CRC samples. The common genes contained 89 up-regulated (DEGs1) and 80 down-regulated genes (DEGs3); and independent DEGs contained 270 down-regulated genes (DEGs4) in diabetes and 144 down-regulated genes (DEGs2) in CRC. In enrichment analysis, the Ribosome pathway was significantly enriched by the independent DEGs. The common genes were mainly enriched in some inflammatory related pathways. Two target genes of Metformin were significantly interacted with six hub genes (HADHB, NDUFS3, TAF1, MYC, HNFF4A, and MAX) with significant changes in expression values (P < 0.05, t-test). To summary, it is suggested that the six hub genes might play important roles in the process of Metformin treatment for diabetes and CRC. However, specific pathology remains to be further studied.

Quantitative ecotoxicity analysis for pesticide mixtures using benchmark dose methodology.

Pesticide mixtures can often be found on crops and in the natural environment due to the usage of multiple pesticides in crop production. However, the toxicity of pesticides is mostly evaluated individually but not jointly. Many studies have pointed out that pesticide mixture may have elevated toxicity compared with its individual counterpart, therefore, it is important to quantify the change in toxicity. Such quantification can provide invaluable information for environmental and ecological risk assessment, and further support risk management to develop appropriate means to mitigate the risk. The objective of this study is to quantify the ecotoxicity of pesticide mixtures composed of different combinations of four pesticides (i.e., Acetemiprid, Carbendazim, Chlorpyrifos, Cyhalothrin) to (1) understand if the co-presence of multiple pesticides will affect the toxicity and (2) to quantitatively approximate the change in toxicity. We first conducted acute toxicity testing and avoidance response testing using earthworms to obtain dose-response data for two different endpoints; then the benchmark dose (BMD) methodology was applied to estimate the toxicity values for the active ingredients of these four pesticides and their mixtures. The BMD analysis results suggest that the ecotoxicity of the active ingredients of the pesticides is very likely to increase when two or more pesticides are used simultaneously, highlighting the importance to consider toxicity of mixtures in the regulatory decision making process. This study demonstrates that the benchmark dose methodology can be a useful tool to quantify the toxicity of chemical mixtures and support cumulative risk assessment accordingly.

Bayesian Hierarchical Structure for Quantifying Population Variability to Inform Probabilistic Health Risk Assessments.

Human variability is a very important factor considered in human health risk assessment for protecting sensitive populations from chemical exposure. Traditionally, to account for this variability, an interhuman uncertainty factor is applied to lower the exposure limit. However, using a fixed uncertainty factor rather than probabilistically accounting for human variability can hardly support probabilistic risk assessment advocated by a number of researchers; new methods are needed to probabilistically quantify human population variability. We propose a Bayesian hierarchical model to quantify variability among different populations. This approach jointly characterizes the distribution of risk at background exposure and the sensitivity of response to exposure, which are commonly represented by model parameters. We demonstrate, through both an application to real data and a simulation study, that using the proposed hierarchical structure adequately characterizes variability across different populations.

Bayesian Quantile Impairment Threshold Benchmark Dose Estimation for Continuous Endpoints.

Quantitative risk assessment often begins with an estimate of the exposure or dose associated with a particular risk level from which exposure levels posing low risk to populations can be extrapolated. For continuous exposures, this value, the benchmark dose, is often defined by a specified increase (or decrease) from the median or mean response at no exposure. This method of calculating the benchmark dose does not take into account the response distribution and, consequently, cannot be interpreted based upon probability statements of the target population. We investigate quantile regression as an alternative to the use of the median or mean regression. By defining the dose-response quantile relationship and an impairment threshold, we specify a benchmark dose as the dose associated with a specified probability that the population will have a response equal to or more extreme than the specified impairment threshold. In addition, in an effort to minimize model uncertainty, we use Bayesian monotonic semiparametric regression to define the exposure-response quantile relationship, which gives the model flexibility to estimate the quantal dose-response function. We describe this methodology and apply it to both epidemiology and toxicology data.

AS3MT, GSTO, and PNP polymorphisms: impact on arsenic methylation and implications for disease susceptibility.

BACKGROUND: Oral exposure to inorganic arsenic (iAs) is associated with adverse health effects. Epidemiological studies suggest differences in susceptibility to these health effects, possibly due to genotypic variation. Genetic polymorphisms in iAs metabolism could lead to increased susceptibility by altering urinary iAs metabolite concentrations. OBJECTIVE: To examine the impact of genotypic polymorphisms on iAs metabolism. METHODS: We screened 360 publications from PubMed and Web of Science for data on urinary mono- and dimethylated arsenic (MMA and DMA) percentages and polymorphic genes encoding proteins that are hypothesized to play roles in arsenic metabolism. The genes we examined were arsenic (+3) methyltransferase (AS3MT), glutathione-s-transferase omega (GSTO), and purine nucleoside phosphorylase (PNP). Relevant data were pooled to determine which polymorphisms are associated across studies with changes in urinary metabolite concentration. RESULTS: In our review, AS3MT polymorphisms rs3740390, rs11191439, and rs11191453 were associated with statistically significant changes in percent urinary MMA. Studies of GSTO polymorphisms did not indicate statistically significant associations with methylation, and there are insufficient data on PNP polymorphisms to evaluate their impact on metabolism. DISCUSSION: Collectively, these data support the hypothesis that AS3MT polymorphisms alter in vivo metabolite concentrations. Preliminary evidence suggests that AS3MT genetic polymorphisms may impact disease susceptibility. GSTO polymorphisms were not associated with iAs-associated health outcomes. Additional data are needed to evaluate the association between PNP polymorphisms and iAs-associated health outcomes. Delineation of these relationships may inform iAs mode(s) of action and the approach for evaluating low-dose health effects for iAs. CONCLUSIONS: Genotype impacts urinary iAs metabolite concentrations and may be a potential mechanism for iAs-related disease susceptibility.

Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data.

The benchmark dose (BMD) approach has gained acceptance as a valuable risk assessment tool, but risk assessors still face significant challenges associated with selecting an appropriate BMD/BMDL estimate from the results of a set of acceptable dose-response models. Current approaches do not explicitly address model uncertainty, and there is an existing need to more fully inform health risk assessors in this regard. In this study, a Bayesian model averaging (BMA) BMD estimation method taking model uncertainty into account is proposed as an alternative to current BMD estimation approaches for continuous data. Using the "hybrid" method proposed by Crump, two strategies of BMA, including both "maximum likelihood estimation based" and "Markov Chain Monte Carlo based" methods, are first applied as a demonstration to calculate model averaged BMD estimates from real continuous dose-response data. The outcomes from the example data sets examined suggest that the BMA BMD estimates have higher reliability than the estimates from the individual models with highest posterior weight in terms of higher BMDL and smaller 90th percentile intervals. In addition, a simulation study is performed to evaluate the accuracy of the BMA BMD estimator. The results from the simulation study recommend that the BMA BMD estimates have smaller bias than the BMDs selected using other criteria. To further validate the BMA method, some technical issues, including the selection of models and the use of bootstrap methods for BMDL derivation, need further investigation over a more extensive, representative set of dose-response data.