Linking empirical estimates of body burden of environmental chemicals and wellness using NHANES data

Open Letter to President Trump. How to Cure the Disease Treatment System? Make It a Health Care System

Clearly the current way we address health and disease in the United States is not working. We now suffer the highest burden of chronic disease in every age group ever in human history. And, at the same time, we have the most expensive health care system ever. Why? Because the actual causes of disease are not being addressed. Eight years ago, when you were first elected president, I gathered together the leadership of natural, integrative, functional, and environmental medicine to provide guidance on what needed to change. Unfortunately, none of our recommendations were addressed, every measure of health and disease burden has worsened, and health care costs continue to increase far beyond the rate of inflation. President Trump, please take a look and consider these recommendations.

HBM4EU results support the Chemicals' Strategy for Sustainability and the Zero-Pollution Action Plan

One of the major goals of the European Human Biomonitoring Initiative (HBM4EU) was to bridge the gap between science and policy by consulting both policy makers and national scientists and generating evidence of the actual exposure of residents to chemicals and whether that exposure would be suggest a potential health risk. Residents' perspectives on chemical exposure and risk were also investigated. HBM4EU's research was designed to answer specific short-term and long-term policy questions at national and European levels, and for its results to directly support regulatory action on chemicals. A strategy was established to prioritise chemicals for analysis in human matrices, with a total of 18 substances/substance groups chosen to be investigated throughout the five-and a -half-year project. HBM4EU produced new evidence of human exposure levels, developed reference values for exposure, investigated determinants of exposure and derived health-based guidance values for those substances. In addition, HBM4EU promoted the use of human biomonitoring data in chemical risk assessment and developed innovative tools and methods linking chemicals to possible health impacts, such as effect biomarkers. Furthermore, HBM4EU advanced understand of effects from combined exposures and methods to identify emerging chemicals. With the aim of supporting policy implementation, science-to-policy workshops were organised, providing opportunities for joint reflection and dialogue on research results. I, and indicators were developed to assess temporal and spatial patterns in the exposure of European population. A sustainable human biomonitoring monitoring framework, producing comparable quality assured data would allow: the evaluation of time trends; the exploration of spatial trends: the evaluation of the influence of socio-economic conditions on chemical exposure. Therefore, such a framework should be included in the European Chemicals' Strategy for Sustainability and the data would support the Zero Pollution Action Plan.

Biomonitoring of exposure to Great Lakes contaminants among licensed anglers and Burmese refugees in Western New York: Toxic metals and persistent organic pollutants, 2010-2015

Between 2010 and 2015, the New York State Department of Health (NYSDOH) conducted a biomonitoring program to gather exposure data on Great Lakes contaminants among licensed anglers and Burmese refugees living in western New York who ate locally caught fish. Four hundred and nine adult licensed anglers and 206 adult Burmese refugees participated in this program. Participants provided blood and urine samples and completed a detailed questionnaire. Herein, we present blood metal levels (cadmium, lead, and total mercury) and serum persistent organic pollutant concentrations [polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), dichlorodiphenyldichloroethylene (DDE), and trans-nonachlor]. Multiple linear regression was applied to investigate the associations between analyte concentrations and indicators of fish consumption (locally caught fish meals, store-bought fish meals, and consuming fish/shellfish in the past week). Licensed anglers consumed a median of 16 locally caught fish meals and 22 store-bought fish meals while Burmese refugees consumed a median of 106 locally caught fish meals and 104 store-bought fish/shellfish meals in the past year. Compared to the general U.S. adult population, licensed anglers had higher blood lead and mercury levels; and Burmese refuges had higher blood cadmium, lead, and mercury, and higher serum DDE levels. Eating more locally caught fish was associated with higher blood lead, blood mercury, and serum ∑PCBs concentrations among licensed anglers. Licensed anglers and Burmese refugees who reported fish/shellfish consumption in the past week had elevated blood mercury levels compared with those who reported no consumption. Among licensed anglers, eating more store-bought fish meals was also associated with higher blood mercury levels. As part of the program, NYSDOH staff provided fish advisory outreach and education to all participants on ways to reduce their exposures, make healthier choices of fish to eat, and waters to fish from. Overall, our findings on exposure levels and fish consumption provide information to support the development and implementation of exposure reduction public health actions.

Integrating deep learning, social networks, and big data for healthcare system

This paper aims to propose a deep learning model based on big data for the healthcare system to predict social network data. Social network users post large amounts of healthcare information on a daily basis and at the same time hospitals and medical laboratories store very large amounts of healthcare data, such as X-rays. The authors provide an architecture that can integrate deep learning, social networks, and big data. Deep learning is one of the most challenging areas of research and is becoming increasingly popular in the health sector. It uses deep analysis to extract knowledge with optimum precision. The proposed architecture consists of three layers: the deep learning layer, the big data layer, and the social networks layer. The big data layer includes data for health care, such as X-ray images. For the deep learning layer, three Convolution Neuronal Network models are proposed for X-ray image classification. As a result, social network layer users can access the proposed system to predict their X-ray image posts.

Description of exposure profiles for seven environmental chemicals in a US population using recursive partition mixture modeling (RPMM)

Biomonitoring studies have shown that humans are exposed to numerous environmental chemicals. Previous work provides limited insights into the dynamic relationship between different chemicals within a population. The objective of this study is to develop an analytical method identifying exposure profiles of seven common environmental chemicals and determine how exposure profiles differ by sociodemographic groups and National Health and Nutrition Examination Survey (NHANES) 2003-2012 cycle year. We used recursive partition mixture modeling (RPMM) to define classes of the population with similar exposure profiles of lead, cadmium, 2,4-dichlorophenol, 2,5-dichlorophenol, bisphenol A (BPA), triclosan, and benzophenone-3 in individuals aged ≥6 years. Additionally, quasibinomial logistic regression was used to examine the association between each class and selected demographic characteristics. Eight exposure profiles were identified. Individuals who clustered together and had the highest chemical exposures were more likely to be older, to be Non-Hispanic Black (NHB) or Other Hispanic (OH), more likely to live below the poverty line, more likely to be male, and more likely to have participated in the earlier NHANES cycle (2003-2004). The developed method described the dynamic relationship between chemicals and shows that this relationship is different for subpopulations based on their sociodemographic characteristics.

Sex-Dependent Influence of Developmental Toxicant Exposure on Group B Streptococcus-Mediated Preterm Birth in a Murine Model

Infectious agents are a significant risk factor for preterm birth (PTB); however, the simple presence of bacteria is not sufficient to induce PTB in most women. Human and animal data suggest that environmental toxicant exposures may act in concert with other risk factors to promote PTB. Supporting this "second hit" hypothesis, we previously demonstrated exposure of fetal mice (F1 animals) to the environmental endocrine disruptor 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) leads to an increased risk of spontaneous and infection-mediated PTB in adult animals. Surprisingly, adult F1_males also confer an enhanced risk of PTB to their control partners. Herein, we used a recently established model of ascending group B Streptococcus (GBS) infection to explore the impact of a maternal versus paternal developmental TCDD exposure on infection-mediated PTB in adulthood. Group B Streptococcus is an important contributor to PTB in women and can have serious adverse effects on their infants. Our studies revealed that although gestation length was reduced in control mating pairs exposed to low-dose GBS, dams were able to clear the infection and bacterial transmission to pups was minimal. In contrast, exposure of pregnant F1_females to the same GBS inoculum resulted in 100% maternal and fetal mortality. Maternal health and gestation length were not impacted in control females mated to F1_males and exposed to GBS; however, neonatal survival was reduced compared to controls. Our data revealed a sex-dependent impact of parental TCDD exposure on placental expression of Toll-like receptor 2 and glycogen production, which may be responsible for the differential impact on fetal and maternal outcomes in response to GBS infection.

An Overview of Literature Topics Related to Current Concepts, Methods, Tools, and Applications for Cumulative Risk Assessment (2007-2016)

Cumulative risk assessments (CRAs) address combined risks from exposures to multiple chemical and nonchemical stressors and may focus on vulnerable communities or populations. Significant contributions have been made to the development of concepts, methods, and applications for CRA over the past decade. Work in both human health and ecological cumulative risk has advanced in two different contexts. The first context is the effects of chemical mixtures that share common modes of action, or that cause common adverse outcomes. In this context two primary models are used for predicting mixture effects, dose addition or response addition. The second context is evaluating the combined effects of chemical and nonchemical (e.g., radiation, biological, nutritional, economic, psychological, habitat alteration, land-use change, global climate change, and natural disasters) stressors. CRA can be adapted to address risk in many contexts, and this adaptability is reflected in the range in disciplinary perspectives in the published literature. This article presents the results of a literature search and discusses a range of selected work with the intention to give a broad overview of relevant topics and provide a starting point for researchers interested in CRA applications.

Identification and Prioritization of Relationships between Environmental Stressors and Adverse Human Health Impacts

BACKGROUND

There are > 80,000 chemicals in commerce with few data available describing their impacts on human health. Biomonitoring surveys, such as the NHANES (National Health and Nutrition Examination Survey), offer one route to identifying possible relationships between environmental chemicals and health impacts, but sparse data and the complexity of traditional models make it difficult to leverage effectively.

OBJECTIVE

We describe a workflow to efficiently and comprehensively evaluate and prioritize chemical-health impact relationships from the NHANES biomonitoring survey studies.

METHODS

Using a frequent itemset mining (FIM) approach, we identified relationships between chemicals and health biomarkers and diseases.

RESULTS

The FIM method identified 7,848 relationships between 219 chemicals and 93 health outcomes/biomarkers. Two case studies used to evaluate the FIM rankings demonstrate that the FIM approach is able to identify published relationships. Because the relationships are derived from the vast majority of the chemicals monitored by NHANES, the resulting list of associations is appropriate for evaluating results from targeted data mining or identifying novel candidate relationships for more detailed investigation.

CONCLUSIONS

Because of the computational efficiency of the FIM method, all chemicals and health effects can be considered in a single analysis. The resulting list provides a comprehensive summary of the chemical/health co-occurrences from NHANES that are higher than expected by chance. This information enables ranking and prioritization on chemicals or health effects of interest for evaluation of published results and design of future studies.

CITATION

Bell SM, Edwards SW. 2015. Identification and prioritization of relationships between environmental stressors and adverse human health impacts. Environ Health Perspect 123:1193-1199; http://dx.doi.org/10.1289/ehp.1409138.

Development and Validation of a Clinical Risk-Assessment Tool Predictive of All-Cause Mortality

In clinical settings, the diagnosis of medical conditions is often aided by measurement of various serum biomarkers through the use of laboratory tests. These biomarkers provide information about different aspects of a patient's health and overall function of multiple organ systems. We have developed a statistical procedure that condenses the information from a variety of health biomarkers into a composite index, which could be used as a risk score for predicting all-cause mortality. It could also be viewed as a holistic measure of overall physiological health status. This health status metric is computed as a function of standardized values of each biomarker measurement, weighted according to their empirically determined relative strength of association with mortality. The underlying risk model was developed using the biomonitoring and mortality data of a large sample of US residents obtained from the National Health and Nutrition Examination Survey (NHANES) and the National Death Index (NDI). Biomarker concentration levels were standardized using spline-based Cox regression models, and optimization algorithms were used to estimate the weights. The predictive accuracy of the tool was optimized by bootstrap aggregation. We also demonstrate how stacked generalization, a machine learning technique, can be used for further enhancement of the prediction power. The index was shown to be highly predictive of all-cause mortality and long-term outcomes for specific health conditions. It also exhibited a robust association with concurrent chronic conditions, recent hospital utilization, and current health status as assessed by self-rated health.

Dioxins, furans and dioxin-like PCBs in human blood: causes or consequences of diabetic nephropathy?

Nephropathy, or kidney disease, is a major, potential complication of diabetes. We assessed the association of 6 chlorinated dibenzo-p-dioxins, 9 chlorinated dibenzofurans and 8 polychlorinated biphenyls (PCBs) in blood with diabetic nephropathy in the 1999-2004 National Health and Nutrition Examination Survey (unweighted N=2588, population estimate=117,658,357). Diabetes was defined as diagnosed or undiagnosed (glycohemoglobin ≥ 6.5%) and nephropathy defined as urinary albumin to creatinine ratio >30 mg/g, representing microalbuminuria or macroalbuminuria. For the 8 chemicals analyzed separately, values above the 75th percentile were considered elevated, whereas for the other 15 compounds values above the maximum limit of detection were considered elevated. Seven of 8 dioxins and dioxin-like compounds, analyzed separately, were found to be associated with diabetic nephropathy. The chemicals associated with diabetic nephropathy were: 1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin; 1,2,3,4,6,7,8,9-Octachlorodibenzo-p-dioxin; 2,3,4,7,8-Pentachlorodibenzofuran; PCB 126; PCB 169; PCB 118; and PCB 156. Three of the 8 dioxins and dioxin-like compounds; 1,2,3,4,6,7,8,9-Octachlorodibenzo-p-dioxin; 2,3,4,7,8-Pentachlorodibenzofuran and PCB 118; expressed as log-transformed continuous variables; were associated with diabetes without nephropathy. When 4 or more of the 23 chemicals were elevated the odds ratios were 7.00 (95% CI=1.80-27.20) for diabetic nephropathy and 2.13 (95% CI=0.95-4.78) for diabetes without nephropathy. Log-transformed toxic equivalency (TEQ) was associated with both diabetic nephropathy, and diabetes without nephropathy, the odds ratios were 2.35 (95% CI=1.57-3.52) for diabetic nephropathy, and 1.44 (95% CI=1.11-1.87) for diabetes without nephropathy. As the kidneys function to remove waste products from the blood, diabetic nephropathy could be either the cause or the consequence (or both) of exposure to dioxins, furans and dioxin-like PCBs.