Personalized exposure assessment: promising approaches for human environmental health research

The Role of Genetic, Environmental, and Dietary Factors in Alzheimer's Disease: A Narrative Review

Alzheimer's disease (AD) is one of the most common and severe forms of dementia and neurodegenerative disease. As life expectancy increases in line with developments in medicine, the elderly population is projected to increase in the next few decades; therefore, an increase in the prevalence of some diseases, such as AD, is also expected. As a result, until a radical treatment becomes available, AD is expected to be more frequently recorded as one of the top causes of death worldwide. Given the current lack of a cure for AD, and the only treatments available being ones that alleviate major symptoms, the identification of contributing factors that influence disease incidence is crucial. In this context, genetic and/or epigenetic factors, mainly environmental, disease-related, dietary, or combinations/interactions of these factors, are assessed. In this review, we conducted a literature search focusing on environmental factors such as air pollution, toxic elements, pesticides, and infectious agents, as well as dietary factors including various diets, vitamin D deficiency, social factors (e.g., tobacco and alcohol use), and variables that are affected by both environmental and genetic factors, such as dietary behavior and gut microbiota. We also evaluated studies on the beneficial effects of antibiotics and diets, such as the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) and Mediterranean diets.

Association between exposure to particulate matter and heart rate variability in vulnerable and susceptible individuals

Particulate matter (PM) exposure can reduce heart rate variability (HRV), a cardiovascular health marker. This study examines PM1.0 (aerodynamic diameters <1 μm), PM2.5 (≥1 μm and <2.5 μm), and PM10 (≥2.5 μm and <10 μm) effects on HRV in patients with environmental diseases as chronic disease groups and vulnerable populations as control groups. PM levels were measured indoors and outdoors for five days in 97 participants, with 24-h HRV monitoring via wearable devices. PM exposure was assessed by categorizing daily cumulative PM concentrations into higher and lower exposure days, while daily average PM concentrations were used for analysis. Results showed significant negative associations between exposure to single and mixtures of different PM metrics and HRV across all groups, particularly in chronic airway disease and higher air pollution exposed groups. These findings highlight that even lower PM levels may reduce HRV, suggesting a need for stricter standards to protect sensitive individuals.

Personal environmental exposure to plasticizers and organophosphate flame retardants using silicone wristbands and urine: Patterns, comparisons, and correlations

Plasticizers (PLs) and organophosphate flame retardants (OPFRs) are ubiquitous in the environment due to their widespread use and potential for leaching from consumer products. Environmental exposure is a critical aspect of the human exposome, revealing complex interactions between environmental contaminants and potential health effects. Silicone wristbands (SWBs) have emerged as a novel and non-invasive sampling device for assessing personal external exposure. In this study, SWBs were used as a proxy to estimate personal dermal adsorption (EDdermal) to PLs and OPFRs in Belgian participants for one week; four morning urine samples were also collected and analyzed for estimated daily intake (EDI). The results of the SWBs samples showed that all the participants were exposed to these chemicals, and the exposure was found to be highest for the legacy and alternative plasticizers (LP and AP), followed by the legacy and emerging OPFRs (LOPFR and EOPFR). In urine samples, the highest levels were observed for metabolites of diethyl phthalate (DEP), di-isobutyl phthalate (DiBP) and di-n-butyl phthalate (DnBP) among LPs and di(2-ethylhexyl) terephthalate (DEHT) for APs. Outliers among the participants indicated that there were other sources of exposure that were not identified. Results showed a significant correlation between EDdermal and EDI for DiBP, tris (2-butoxyethyl) phosphate (TBOEP) and triphenyl phosphate (TPhP). These correlations indicated their suitability for predicting exposure via SWB monitoring for total chemical exposure. The results of this pilot study advance our understanding of SWB sampling and its relevance for predicting aggregate environmental chemical exposures, while highlighting the potential of SWBs as low-cost, non-invasive personal samplers for future research. This innovative approach has the potential to advance the assessment of environmental exposures and their impact on public health.

Using time-resolved monitor wearing data to study the effect of clean cooking interventions on personal air pollution exposures

BACKGROUND

Personal monitoring can estimate individuals' exposures to environmental pollutants; however, accuracy depends on consistent monitor wearing, which is under evaluated.

OBJECTIVE

To study the association between device wearing and personal air pollution exposure.

METHODS

Using personal device accelerometry data collected in the context of a randomized cooking intervention in Ghana with three study arms (control, improved biomass, and liquified petroleum gas (LPG) arms; N = 1414), we account for device wearing to infer parameters of PM2.5 and CO exposure.

RESULTS

Device wearing was positively associated with exposure in the control and improved biomass arms, but weakly in the LPG arm. Inferred community-level air pollution was similar across study arms (~45 μg/m3). The estimated direct contribution of individuals' cooking to PM2.5 exposure was 64 μg/m3 for the control arm, 74 μg/m3 for improved biomass, and 6 μg/m3 for LPG. Arm-specific average PM2.5 exposure at near-maximum wearing was significantly lower in the LPG arm as compared to the improved biomass and control arms. Analysis of personal CO exposure mirrored PM2.5 results.

CONCLUSIONS

Personal monitor wearing was positively associated with average air pollution exposure, emphasizing the importance of high device wearing during monitoring periods and directly assessing device wearing for each deployment.

SIGNIFICANCE

We demonstrate that personal monitor wearing data can be used to refine exposure estimates and infer unobserved parameters related to the timing and source of environmental exposures.

IMPACT STATEMENTS

In a cookstove trial among pregnant women, time-resolved personal air pollution device wearing data were used to refine exposure estimates and infer unobserved exposure parameters, including community-level air pollution, the direct contribution of cooking to personal exposure, and the effect of clean cooking interventions on personal exposure. For example, in the control arm, while average 48 h personal PM2.5 exposure was 77 μg/m3, average predicted exposure at near-maximum daytime device wearing was 108 μg/m3 and 48 μg/m3 at zero daytime device wearing. Wearing-corrected average 48 h personal PM2.5 exposures were 50% lower in the LPG arm than the control and improved biomass and inferred direct cooking contributions to personal PM2.5 from LPG were 90% lower than the other arms. Our recommendation is that studies assessing personal exposures should examine the direct association between device wearing and estimated mean personal exposure.

Design and Progress of Child Health Assessments at Community Support Centers in the Birth and Three-Generation Cohort Study of the Tohoku Medical Megabank Project

The Tohoku Medical Megabank Project (TMM) has been conducting a birth and three-generation cohort study (the BirThree Cohort Study). We recruited 73,529 pregnant women and their family members for this cohort study, which included 23,143 newborns and 9,459 of their siblings. We designed and are in the process of conducting three-step health assessments for each newborn at approximately ages of 5, 10 and 16. These health assessments are administered at seven community support centers. Trained genome medical research coordinators conduct physical examinations of and collect biological specimens from each participant. The Sendai Children's Health Square has been established as the headquarters for these child health assessments and is utilized to accumulate knowledge that can facilitate the proper practice of child health assessments. We designed all the relevant health assessments facilities to allow parents and their children to participate in the health assessments concomitantly. Our centers serve as places where child participants and their parents can feel at ease as a result of the implementation of safety measures and child hospitality measures. The TMM BirThree Cohort Study is in the process of conducting strategically detailed health assessments and genome analysis, which can facilitate studies concerning the gene-environment interactions relevant to noncommunicable diseases. Through these operations, our study allows for a significant depth of data to be collected in terms of the number of biospecimens under study and the comprehensiveness of both basic and clinical data alongside relevant family information.

A SWOT analysis of contemporary gaps and a possible diagnostic tool for environmental health in an upper-middle income country: a case study of South Africa

This study evaluated the state of environmental health in the Republic of South Africa using the strengths, weaknesses, opportunities, and threats (SWOT) analysis framework. The internal and external factor evaluations were conducted by reviewing existing literature searched using a combination of keywords and boolean functions. The weighted score for the strengths and weaknesses was 0.79 and 1.04, respectively. The external factor evaluations findings revealed that environmental health has an equal amount of threats and opportunities, however, the threats outweigh the opportunities. The total weighted score for the internal and external factor evaluations was 1.83 and 2.25, respectively, which were below the average value of 2.5. The internal-external factors matrix indicated that environmental health in the Republic of South Africa needs to be refocused and re-aligned to public health protection. Failure to address the weaknesses and threats and optimise the strengths and opportunities may further weaken the provision of environmental health services.

The exposome in rheumatoid arthritis

The exposome integrates the variety and accumulation of exposures (external and internal) to which an individual is submitted to from conception to death. Exposome may therefore be a useful tool for understanding the diversity of these factors and their role in the pathophysiology of rheumatoid arthritis (RA). Life is perceived as a continuum of cumulative changes, with key periods of disruption (e.g. birth, adolescence, pregnancy, prolonged treatment). The combination of these changes and the external signals that cause them constitute an individual's exposome, which is constantly changing and expanding throughout life. Thus, measuring the exposome requires specific tools and approaches as well as a global perspective. RA, a complex, heterogeneous, pro-inflammatory autoimmune disease with a genetic component and for which a large number of environmental factors have already been incriminated is an appropriate field of application for the exposome. The aim of this review is to define the exposome concept, outline the different analytic tools available for its study and finally apply them to the field of RA.

Integrating Multiscale Geospatial Environmental Data into Large Population Health Studies: Challenges and Opportunities

Quantifying the exposome is key to understanding how the environment impacts human health and disease. However, accurately, and cost-effectively quantifying exposure in large population health studies remains a major challenge. Geospatial technologies offer one mechanism to integrate high-dimensional environmental data into epidemiology studies, but can present several challenges. In June 2021, the National Institute of Environmental Health Sciences (NIEHS) held a workshop bringing together experts in exposure science, geospatial technologies, data science and population health to address the need for integrating multiscale geospatial environmental data into large population health studies. The primary objectives of the workshop were to highlight recent applications of geospatial technologies to examine the relationships between environmental exposures and health outcomes; identify research gaps and discuss future directions for exposure modeling, data integration and data analysis strategies; and facilitate communications and collaborations across geospatial and population health experts. This commentary provides a high-level overview of the scientific topics covered by the workshop and themes that emerged as areas for future work, including reducing measurement errors and uncertainty in exposure estimates, and improving data accessibility, data interoperability, and computational approaches for more effective multiscale and multi-source data integration, along with potential solutions.

Short-, Mid-, and Long-Term Associations Between PM2.5 and Stroke Incidence in Taiwan

OBJECTIVE

To investigate the association between the risk of stroke and exposure to particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) over various exposure periods.

METHODS

This was a nationwide population-based case-control study in which 10,035 incident patients with a primary diagnosis of ischemic stroke each were matched with two randomly selected controls for sex, age, Charlson Comorbidity Index, year of stroke diagnosis, and level of urbanization. Multiple logistic models adjusted for potential confounders were used to assess the association of PM2.5 with ischemic stroke incidence.

RESULTS

There were significant short-term, medium-term, and long-term relationships between PM2.5 exposure and ischemic stroke incidence.

CONCLUSIONS

This study supports existing evidence that PM2.5 should be considered a risk factor for ischemic stroke.

Determinants of Childhood Zoonotic Enteric Infections in a Semirural Community of Quito, Ecuador

Domestic animals in the household environment have the potential to affect a child's carriage of zoonotic enteric pathogens and risk of diarrhea. This study examines the risk factors associated with pediatric diarrhea and carriage of zoonotic enteric pathogens among children living in communities where smallholder livestock production is prevalent. We conducted an observational study of children younger than 5 years that included the analysis of child (n = 306) and animal (n = 480) fecal samples for Campylobacter spp., atypical enteropathogenic Escherichia coli, Shiga toxin-producing E. coli, Salmonella spp., Yersinia spp., Cryptosporidium parvum, and Giardia lamblia. Among these seven pathogens, Giardia was the most commonly identified pathogen among children and animals in the same household, most of which was found in child-dog pairs. Campylobacter spp. was also relatively common within households, particularly among child-chicken and child-guinea pig pairs. We used multivariable Poisson regression models to assess risk factors associated with a child being positive for at least one zoonotic enteric pathogen or having diarrhea during the last week. Children who interacted with domestic animals-a behavior reported by nearly three-quarters of households owning animals-were at an increased risk of colonization with at least one zoonotic enteric pathogen (prevalence ratio [PR] = 1.56, 95% CI: 1.00-2.42). The risk of diarrhea in the last seven days was elevated but not statistically significant (PR = 2.27, CI: 0.91, 5.67). Interventions that aim to reduce pediatric exposures to enteric pathogens will likely need to be incorporated with approaches that remove animal fecal contamination from the domestic environment and encourage behavior change aimed at reducing children's contact with animal feces through diverse exposure pathways.

Fine-Scale Modeling of Individual Exposures to Ambient PM2.5, EC, NOx, CO for the Coronary Artery Disease and Environmental Exposure (CADEE) Study

Air pollution epidemiological studies often use outdoor concentrations from central-site monitors as exposure surrogates, which can induce measurement error. The goal of this study was to improve exposure assessments of ambient fine particulate matter (PM2.5), elemental carbon (EC), nitrogen oxides (NOx), and carbon monoxide (CO) for a repeated measurements study with 15 individuals with coronary artery disease in central North Carolina called the Coronary Artery Disease and Environmental Exposure (CADEE) Study. We developed a fine-scale exposure modeling approach to determine five tiers of individual-level exposure metrics for PM2.5, EC, NOx, CO using outdoor concentrations, on-road vehicle emissions, weather, home building characteristics, time-locations, and time-activities. We linked an urban-scale air quality model, residential air exchange rate model, building infiltration model, global positioning system (GPS)-based microenvironment model, and accelerometer-based inhaled ventilation model to determine residential outdoor concentrations (Cout_home, Tier 1), residential indoor concentrations (Cin_home, Tier 2), personal outdoor concentrations (Cout_personal, Tier 3), exposures (E, Tier 4), and inhaled doses (D, Tier 5). We applied the fine-scale exposure model to determine daily 24-h average PM2.5, EC, NOx, CO exposure metrics (Tiers 1-5) for 720 participant-days across the 25 months of CADEE. Daily modeled metrics showed considerable temporal and home-to-home variability of Cout_home and Cin_home (Tiers 1-2) and person-to-person variability of Cout_personal, E, and D (Tiers 3-5). Our study demonstrates the ability to apply an urban-scale air quality model with an individual-level exposure model to determine multiple tiers of exposure metrics for an epidemiological study, in support of improving health risk assessments.

Reusable nano-BG-FET for point-of-care estimation of ammonia and urea in human urine

A back-gate-field-effect-transistor (BG-FET) has been developed to selectively detect ammonia and urea. The BG-FET was prepared on a p-type Si substrate with an n-type channel of CdS-TiO₂ nanocomposite and poly-methyl methacrylate film as dielectric layer. The reusability of the sensor was ensured by putting it as a cover to a chamber where samples were detected. The BG-FET showed an increase in drain current with the increase in ammonia release from chamber because higher numbers of charge carriers were created when ammonia adsorped on CdS-TiO₂ nanostructures. Control experiments suggested that the variation in current-to-voltage response of BG-FET could also be calibrated to measure the activity of a host of other hazardous gases. The lowest concentration of ammonia detected was ∼0.85 ppm with a response time of 30 s at a gate voltage of 0.5 V or less, which were superior than available field effect transistors ammonia sensors. Addition of urease in urine liberated ammonia equivalent to urea content in urine, which could be detected by the proposed BGFET. The urea-urease enzyme catalysis reaction made the sensor specific in detecting the biomarker. The accuracy, sensitivity, and reusability of the device was found to be suitable to develop a point-of-care testing device for ammonia and urea detection.

A biplot correlation range for group-wise metabolite selection in mass spectrometry

Background

Analytic methods are available to acquire extensive metabolic information in a cost-effective manner for personalized medicine, yet disease risk and diagnosis mostly rely upon individual biomarkers based on statistical principles of false discovery rate and correlation. Due to functional redundancies and multiple layers of regulation in complex biologic systems, individual biomarkers, while useful, are inherently limited in disease characterization. Data reduction and discriminant analysis tools such as principal component analysis (PCA), partial least squares (PLS), or orthogonal PLS (O-PLS) provide approaches to separate the metabolic phenotypes, but do not offer a statistical basis for selection of group-wise metabolites as contributors to metabolic phenotypes.

Methods

We present a dimensionality-reduction based approach termed ‘biplot correlation range (BCR)’ that uses biplot correlation analysis with direct orthogonal signal correction and PLS to provide the group-wise selection of metabolic markers contributing to metabolic phenotypes.

Results

Using a simulated multiple-layer system that often arises in complex biologic systems, we show the feasibility and superiority of the proposed approach in comparison of existing approaches based on false discovery rate and correlation. To demonstrate the proposed method in a real-life dataset, we used LC-MS based metabolomics to determine spectrum of metabolites present in liver mitochondria from wild-type (WT) mice and thioredoxin-2 transgenic (TG) mice. We select discriminatory variables in terms of increased score in the direction of class identity using BCR. The results show that BCR provides means to identify metabolites contributing to class separation in a manner that a statistical method by false discovery rate or statistical total correlation spectroscopy can hardly find in complex data analysis for predictive health and personalized medicine.

Electronic supplementary material

The online version of this article (10.1186/s13040-019-0191-2) contains supplementary material, which is available to authorized users.

A novel experimental approach for liver analysis in rats exposed to Bisphenol A by means of LC-mass spectrometry and infrared spectroscopy

An innovative complementary approach using a liquid chromatographic-mass spectrometer method and infrared spectroscopy is proposed for measuring internal biological exposure to dangerous chemical contaminants and for monitoring biochemical changes in target organs. The proposed methodologies were validated and applied in the case of rats exposed to low-doses of Bisphenol A (BPA). A liquid chromatographic method coupled to a tandem mass spectrometer was used in order to measure BPA concentration in rat livers. BPA was detected at different levels in all liver samples from BPA-treated rats, although the exposure dose was the same in all treated animals, and also from control rats, highlighting the difficulties in eliminating external uncontrolled exposure and the need for internal biological monitoring. Fourier Transform Infrared analysis was applied to detect structural changes occurring in several molecules (lipids, proteins, carbohydrates and nucleic acids) as well as the presence of specific metabolic processes. The spectroscopic analyses clearly demonstrated a different lipid composition more than an evident lipid accumulation and a glycogen accumulation decrease, revealing a metabolic disturbance in livers with a normal histological aspect. These results demonstrated the potential of an integrated approach based on mass spectrometry and infrared spectroscopy to evaluate at an early stage the hepatotoxic effect of BPA exposure in an animal model. This approach can be usefully exploited in all the investigations aimed to provide better information concerning the interrelationships between contaminant exposure, dose, and health effects.

Synergistic Environmental Exposures and the Airways Capturing Complexity in Humans: An Underappreciated World of Complex Exposures

Paradoxically, the vast majority of research models intended to understand the relationship between exogenous exposures and lung disease are reduced to a single inhalant. This approach is understandable given the practical challenges of investigation, but it is problematic in terms of translation to the real-world human condition. Furthermore, use of data from such models can lead to underestimation of effect, which may adversely influence regulatory imperatives to protect public health based on the most robust information. Efforts to incrementally introduce layers of complexity to observational and experimental systems have revealed pathophysiology previously "hidden" within simplified models. Capturing the effects of co-exposure to traffic-related air pollution and allergens is a paradigmatic example and illustrates the influence of co-exposures across a plethora of clinical and subclinical end points within the respiratory tract. From DNA methylation in the epithelium, to inflammatory mediators and allergen-specific antibodies in the airway, to airflow limitation and symptoms, the addition of a common second exposure induces profound changes. In addition, genetic variation significantly alters the product of these relationships, and capturing multidimensional interactions may reveal susceptible populations who are particularly affected by these exposures and may merit focused measures for protection. Collectively, better modeling, and ultimately deeper knowledge, of these complex relationships has important implications for personalized health and prevention, development and refinement of pharmacologic agents, and public health responses to climate change and the staggering burden of pollution-driven disease worldwide.

Assessing the Exposome with External Measures: Commentary on the State of the Science and Research Recommendations

The exposome comprises all environmental exposures that a person experiences from conception throughout the life course. Here we review the state of the science for assessing external exposures within the exposome. This article reviews (a) categories of exposures that can be assessed externally, (b) the current state of the science in external exposure assessment, (c) current tools available for external exposure assessment, and (d) priority research needs. We describe major scientific and technological advances that inform external assessment of the exposome, including geographic information systems; remote sensing; global positioning system and geolocation technologies; portable and personal sensing, including smartphone-based sensors and assessments; and self-reported questionnaire assessments, which increasingly rely on Internet-based platforms. We also discuss priority research needs related to methodological and technological improvement, data analysis and interpretation, data sharing, and other practical considerations, including improved assessment of exposure variability as well as exposure in multiple, critical life stages.

Detecting Spatial Patterns of Disease in Large Collections of Electronic Medical Records Using Neighbor-Based Bootstrapping

We introduce a method called neighbor-based bootstrapping (NB2) that can be used to quantify the geospatial variation of a variable. We applied this method to an analysis of the incidence rates of disease from electronic medical record data (International Classification of Diseases, Ninth Revision codes) for ∼100 million individuals in the United States over a period of 8 years. We considered the incidence rate of disease in each county and its geospatially contiguous neighbors and rank ordered diseases in terms of their degree of geospatial variation as quantified by the NB2 method. We show that this method yields results in good agreement with established methods for detecting spatial autocorrelation (Moran's I method and kriging). Moreover, the NB2 method can be tuned to identify both large area and small area geospatial variations. This method also applies more generally in any parameter space that can be partitioned to consist of regions and their neighbors.

The need for non- or minimally-invasive biomonitoring strategies and the development of pharmacokinetic/pharmacodynamic models for quantification

Advancements in Exposure Science involving the development and deployment of biomarkers of exposure and biological response are anticipated to significantly (and positively) influence health outcomes associated with occupational, environmental and clinical exposure to chemicals/drugs. To achieve this vision, innovative strategies are needed to develop multiplex sensor platforms capable of quantifying individual and mixed exposures (i.e. systemic dose) by measuring biomarkers of dose and biological response in readily obtainable (non-invasive) biofluids. Secondly, the use of saliva (alternative to blood) for biomonitoring coupled with the ability to rapidly analyze multiple samples in real-time offers an innovative opportunity to revolutionize biomonitoring assessments. In this regard, the timing and number of samples taken for biomonitoring will not be limited as is currently the case. In addition, real-time analysis will facilitate identification of work practices or conditions that are contributing to increased exposures and will make possible a more rapid and successful intervention strategy. The initial development and application of computational models for evaluation of saliva/blood analyte concentration at anticipated exposure levels represents an important opportunity to establish the limits of quantification and robustness of multiplex sensor systems by exploiting a unique computational modeling framework. The use of these pharmacokinetic models will also enable prediction of an exposure dose based on the saliva/blood measurement. This novel strategy will result in a more accurate prediction of exposures and, once validated, can be employed to assess dosimetry to a broad range of chemicals in support of biomonitoring and epidemiology studies.

Integrating ion mobility spectrometry into mass spectrometry-based exposome measurements: what can it add and how far can it go?

Measuring the exposome remains a challenge due to the range and number of anthropogenic molecules that are encountered in our daily lives, as well as the complex systemic responses to these exposures. One option for improving the coverage, dynamic range and throughput of measurements is to incorporate ion mobility spectrometry (IMS) into current MS-based analytical methods. The implementation of IMS in exposomics studies will lead to more frequent observations of previously undetected chemicals and metabolites. LC-IMS-MS will provide increased overall measurement dynamic range, resulting in detections of lower abundance molecules. Alternatively, the throughput of IMS-MS alone will provide the opportunity to analyze many thousands of longitudinal samples over lifetimes of exposure, capturing evidence of transitory accumulations of chemicals or metabolites. The volume of data corresponding to these new chemical observations will almost certainly outpace the generation of reference data to enable their confident identification. In this perspective, we briefly review the state-of-the-art in measuring the exposome, and discuss the potential use for IMS-MS and the physico-chemical property of collisional cross section in both exposure assessment and molecular identification.

SPE-IMS-MS: An automated platform for sub-sixty second surveillance of endogenous metabolites and xenobiotics in biofluids

Characterization of endogenous metabolites and xenobiotics is essential to deconvoluting the genetic and environmental causes of disease. However, surveillance of chemical exposure and disease-related changes in large cohorts requires an analytical platform that offers rapid measurement, high sensitivity, efficient separation, broad dynamic range, and application to an expansive chemical space. Here, we present a novel platform for small molecule analyses that addresses these requirements by combining solid-phase extraction with ion mobility spectrometry and mass spectrometry (SPE-IMS-MS). This platform is capable of performing both targeted and global measurements of endogenous metabolites and xenobiotics in human biofluids with high reproducibility (CV 6 3%), sensitivity (LODs in the pM range in biofluids) and throughput (10-s sample-to-sample duty cycle). We report application of this platform to the analysis of human urine from patients with and without type 1 diabetes, where we observed statistically significant variations in the concentration of disaccharides and previously unreported chemical isomers. This SPE-IMS-MS platform overcomes many of the current challenges of large-scale metabolomic and exposomic analyses and offers a viable option for population and patient cohort screening in an effort to gain insights into disease processes and human environmental chemical exposure.

Enacting the molecular imperative: How gene-environment interaction research links bodies and environments in the post-genomic age

Despite a proclaimed shift from 'nature versus nurture' to 'genes and environment' paradigms within biomedical and genomic science, capturing the environment and identifying gene-environment interactions (GEIs) has remained a challenge. What does 'the environment' mean in the post-genomic age? In this paper, we present qualitative data from a study of 33 principal investigators funded by the U.S. National Institutes of Health to conduct etiological research on three complex diseases (cancer, cardiovascular disease and diabetes). We examine their research practices and perspectives on the environment through the concept of molecularization: the social processes and transformations through which phenomena (diseases, identities, pollution, food, racial/ethnic classifications) are re-defined in terms of their molecular components and described in the language of molecular biology. We show how GEI researchers' expansive conceptualizations of the environment ultimately yield to the imperative to molecularize and personalize the environment. They seek to 'go into the body' and re-work the boundaries between bodies and environments. In the process, they create epistemic hinges to facilitate a turn from efforts to understand social and environmental exposures outside the body, to quantifying their effects inside the body. GEI researchers respond to these emergent imperatives with a mixture of excitement, ambivalence and frustration. We reflect on how GEI researchers struggle to make meaning of molecules in their work, and how they grapple with molecularization as a methodological and rhetorical imperative as well as a process transforming biomedical research practices.

Air Pollution Exposure Model for Individuals (EMI) in Health Studies: Evaluation for Ambient PM2.5 in Central North Carolina

Air pollution health studies of fine particulate matter (diameter ≤2.5 μm, PM2.5) often use outdoor concentrations as exposure surrogates. Failure to account for variability of indoor infiltration of ambient PM2.5 and time indoors can induce exposure errors. We developed and evaluated an exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. We linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (Tier 2), indoor concentrations (Tier 3), personal exposure factors (Tier 4), and personal exposures (Tier 5) for ambient PM2.5. Using cross-validation, individual predictions were compared to 591 daily measurements from 31 homes (Tiers 1-3) and participants (Tiers 4-5) in central North Carolina. Median absolute differences were 39% (0.17 h(-1)) for Tier 1, 18% (0.10) for Tier 2, 20% (2.0 μg/m(3)) for Tier 3, 18% (0.10) for Tier 4, and 20% (1.8 μg/m(3)) for Tier 5. The capability of EMI could help reduce the uncertainty of ambient PM2.5 exposure metrics used in health studies.

Is uncertainty in complex disease epidemiology resolvable?

The imposed limitations on what we can know about nature have been long recognized. Yet in the field of epidemiology a futile search for lifestyle-related risk factors for common chronic diseases continues unabated. This has led to the production of a growing body of evidence about potential lifestyle risk factors that tend to be marginal, contradictory, irreproducible, or hard to interpret. While epidemiologists are calling for a more refined methodology, I argue that our limitation in studying complex diseases is insurmountable. This is because the study of lifestyle-related small risks requires accurate measurement of multiple behaviors-exposures over a long period of time. It is also because in complex systems such as population's health, the effect of rich interactions between its parts cannot be predicted based on traditional causal models of epidemiology. Within complex systems, understanding the interactions between system components can be more important than the contribution of each to disease risk.

Considerations for Using Genetic and Epigenetic Information in Occupational Health Risk Assessment and Standard Setting

Risk assessment forms the basis for both occupational health decision-making and the development of occupational exposure limits (OELs). Although genetic and epigenetic data have not been widely used in risk assessment and ultimately, standard setting, it is possible to envision such uses. A growing body of literature demonstrates that genetic and epigenetic factors condition biological responses to occupational and environmental hazards or serve as targets of them. This presentation addresses the considerations for using genetic and epigenetic information in risk assessments, provides guidance on using this information within the classic risk assessment paradigm, and describes a framework to organize thinking about such uses. The framework is a 4 × 4 matrix involving the risk assessment functions (hazard identification, dose-response modeling, exposure assessment, and risk characterization) on one axis and inherited and acquired genetic and epigenetic data on the other axis. The cells in the matrix identify how genetic and epigenetic data can be used for each risk assessment function. Generally, genetic and epigenetic data might be used as endpoints in hazard identification, as indicators of exposure, as effect modifiers in exposure assessment and dose-response modeling, as descriptors of mode of action, and to characterize toxicity pathways. Vast amounts of genetic and epigenetic data may be generated by high-throughput technologies. These data can be useful for assessing variability and reducing uncertainty in extrapolations, and they may serve as the foundation upon which identification of biological perturbations would lead to a new paradigm of toxicity pathway-based risk assessments.

Considerations for Using Genetic and Epigenetic Information in Occupational Health Risk Assessment and Standard Setting

Risk assessment forms the basis for both occupational health decision-making and the development of occupational exposure limits (OELs). Although genetic and epigenetic data have not been widely used in risk assessment and ultimately, standard setting, it is possible to envision such uses. A growing body of literature demonstrates that genetic and epigenetic factors condition biological responses to occupational and environmental hazards or serve as targets of them. This presentation addresses the considerations for using genetic and epigenetic information in risk assessments, provides guidance on using this information within the classic risk assessment paradigm, and describes a framework to organize thinking about such uses. The framework is a 4 × 4 matrix involving the risk assessment functions (hazard identification, dose-response modeling, exposure assessment, and risk characterization) on one axis and inherited and acquired genetic and epigenetic data on the other axis. The cells in the matrix identify how genetic and epigenetic data can be used for each risk assessment function. Generally, genetic and epigenetic data might be used as endpoints in hazard identification, as indicators of exposure, as effect modifiers in exposure assessment and dose-response modeling, as descriptors of mode of action, and to characterize toxicity pathways. Vast amounts of genetic and epigenetic data may be generated by high-throughput technologies. These data can be useful for assessing variability and reducing uncertainty in extrapolations, and they may serve as the foundation upon which identification of biological perturbations would lead to a new paradigm of toxicity pathway-based risk assessments.

Strengths and weaknesses of Global Positioning System (GPS) data-loggers and semi-structured interviews for capturing fine-scale human mobility: findings from Iquitos, Peru

Quantifying human mobility has significant consequences for studying physical activity, exposure to pathogens, and generating more realistic infectious disease models. Location-aware technologies such as Global Positioning System (GPS)-enabled devices are used increasingly as a gold standard for mobility research. The main goal of this observational study was to compare and contrast the information obtained through GPS and semi-structured interviews (SSI) to assess issues affecting data quality and, ultimately, our ability to measure fine-scale human mobility. A total of 160 individuals, ages 7 to 74, from Iquitos, Peru, were tracked using GPS data-loggers for 14 days and later interviewed using the SSI about places they visited while tracked. A total of 2,047 and 886 places were reported in the SSI and identified by GPS, respectively. Differences in the concordance between methods occurred by location type, distance threshold (within a given radius to be considered a match) selected, GPS data collection frequency (i.e., 30, 90 or 150 seconds) and number of GPS points near the SSI place considered to define a match. Both methods had perfect concordance identifying each participant's house, followed by 80-100% concordance for identifying schools and lodgings, and 50-80% concordance for residences and commercial and religious locations. As the distance threshold selected increased, the concordance between SSI and raw GPS data increased (beyond 20 meters most locations reached their maximum concordance). Processing raw GPS data using a signal-clustering algorithm decreased overall concordance to 14.3%. The most common causes of discordance as described by a sub-sample (n=101) with whom we followed-up were GPS units being accidentally off (30%), forgetting or purposely not taking the units when leaving home (24.8%), possible barriers to the signal (4.7%) and leaving units home to recharge (4.6%). We provide a quantitative assessment of the strengths and weaknesses of both methods for capturing fine-scale human mobility.

A retrospective approach to assess human health risks associated with growing air pollution in urbanized area of Thar Desert, western Rajasthan, India

: Air pollution has been a matter of great concern globally because of the associated health risks to individuals. The situation is getting worse in developing countries with more urbanization, industrialization and more importantly the rapidly growing population posing a threat to human life in the form of pulmonary, cardiovascular, carcinogenic or asthmatic diseases by accumulating toxic pollutants, harmful gases, metals, hydrocarbons etc.

OBJECTIVE

The present study was undertaken to assess the magnitude of ambient air pollutants and their human health risks like respiratory ailments, infectious diseases, cardiovascular diseases and cancer using a Retrospective Approach of Bart Ostra.

METHODOLOGY

The parameters PM2.5, PM10, NOx, SO2, NH3 and O3 were monitored at all selected study sites monitored through a high volume sampler (APM 451 Envirotech, Envirotech Instruments Pvt. Ltd., New Delhi, India). Retrospective Approach was used for assessment of risk factors and disease burden of respiratory and cardiopulmonary health problems.

RESULTS

Environmental burden of disease showed that the problem of health related to air pollution is a main concern particularly in the growing cities of India. High to critical level of air pollution including PM10, PM2.5, NOx, SO2, NH3 and O3 was observed in all seasons at traffic intersections and commercial sites. The respiratory infections (25% incidence in population exposed to indoor smoke problems) and a prevalence of asthma/COPD (4.4%) in households exposed to high vehicular pollution along with signs of coronary artery/heart disease and/or hypertension and cancers (37.9-52.2%), were reported requiring preventive measures.

CONCLUSION

The study reflects a great concern for the mankind with the need of having streamline ways to limit air pollution and emphasize upon efficiently determining the risk of illness upon exposure to air pollution.

A curated review of recent literature of biomarkers used for assessing air pollution exposures and effects in humans

This is a cross-sectional review of biomarkers used in air pollution research from January 2009 through December 2012. After an initial keyword search in PubMed retrieving 426 articles, a comprehensive abstract review identified 54 articles of experimental design that used biomarkers of exposure or effect in human studies in the area of air pollution research during this specified time period. A thorough bibliographic search of the included articles retrieved an additional 65 articles meeting the inclusion criteria. This review presents these 119 studies and the 234 biomarkers employed in these air pollution research investigations. Data presented are 70 biomarkers of exposure with 54% relating to polycyclic aromatic hydrocarbons, 36% volatile organic carbons, and 10% classified as other. Of the 164 biomarkers of effect, 91 and 130 were used in investigating effects of short-term and chronic exposure, respectively. Results of biomarkers used in short-term exposure describe different lag times and pollutant components such as primary and secondary pollutants, and particle number associated with corresponding physiological mechanisms including airway inflammation, neuroinflammation, ocular, metabolic, early endothelial dysfunction, coagulation, atherosclerosis, autonomic nervous system, oxidative stress, and DNA damage. The review presents three different exposure scenarios of chronic, occupational, and extreme exposure scenarios (indoor cooking) with associated biomarker findings presented in three broad categories of (1) immune profile, (2) oxidative stress, and (3) DNA damage. This review offers a representation of the scope of data being explored by air pollution researchers through the use of biomarkers and has deliberately been restricted to this particular subject rather than an extensive or in-depth review. This article provides a contextualization of air pollution studies conducted with biomarkers in human subjects in given areas while also integrating this complex body of information to offer a useful review for investigators in this field of study.

Genetic risk prediction: individualized variability in susceptibility to toxicants

Genetic risk prediction uses genetic data to individualize prediction of outcome or effect from a known harmful toxicant. Several examples of toxicogenetics (usually binary traits) are discussed, reflecting largely Mendelian traits before the Human Genome Project began in 1990. Numerous complexities of the genome and what constitutes "a gene" have emerged during these past two decades. Examples of toxicogenomics (continuous outcomes, gradients) are examined. Most xenobiotic-induced environmental diseases resemble human complex diseases or other multifactorial traits such as height; these traits result from hundreds of low-effect genes. Consequently, uncovering an association between a trait and a genetic variant in a large cohort can provide important information about underlying biology; however, screening for a specific variant in an individual worker or patient has poor predictive value and little clinical utility. Individualized risk assessment for toxicants that cause environmental diseases, although a lofty goal, remains to be achieved.

Design and analysis issues in gene and environment studies

Both nurture (environmental) and nature (genetic factors) play an important role in human disease etiology. Traditionally, these effects have been thought of as independent. This perspective is ill informed for non-mendelian complex disorders which result as an interaction between genetics and environment. To understand health and disease we must study how nature and nurture interact. Recent advances in human genomics and high-throughput biotechnology make it possible to study large numbers of genetic markers and gene products simultaneously to explore their interactions with environment. The purpose of this review is to discuss design and analytic issues for gene-environment interaction studies in the "-omics" era, with a focus on environmental and genetic epidemiological studies. We present an expanded environmental genomic disease paradigm. We discuss several study design issues for gene-environmental interaction studies, including confounding and selection bias, measurement of exposures and genotypes. We discuss statistical issues in studying gene-environment interactions in different study designs, such as choices of statistical models, assumptions regarding biological factors, and power and sample size considerations, especially in genome-wide gene-environment studies. Future research directions are also discussed.

Predicting Adult Pulmonary Ventilation Volume and Wearing Compliance by On-Board Accelerometry During Personal Level Exposure Assessments

BACKGROUND

Metabolic functions typically increase with human activity, but optimal methods to characterize activity levels for real-time predictions of ventilation volume (l/min) during exposure assessments have not been available. Could tiny, triaxial accelerometers be incorporated into personal level monitors to define periods of acceptable wearing compliance, and allow the exposures (μg/m3) to be extended to potential doses in μg/min/kg of body weight?

OBJECTIVES

In a pilot effort, we tested: 1) whether appropriately-processed accelerometer data could be utilized to predict compliance and in linear regressions to predict ventilation volumes in real time as an on-board component of personal level exposure sensor systems, and 2) whether locating the exposure monitors on the chest in the breathing zone, provided comparable accelerometric data to other locations more typically utilized (waist, thigh, wrist, etc.).

METHODS

Prototype exposure monitors from RTI International and Columbia University were worn on the chest by a pilot cohort of adults while conducting an array of scripted activities (all <10 METS), spanning common recumbent, sedentary, and ambulatory activity categories. Referee Wocket accelerometers that were placed at various body locations allowed comparison with the chest-located exposure sensor accelerometers. An Oxycon Mobile mask was used to measure oral-nasal ventilation volumes in-situ. For the subset of participants with complete data (n= 22), linear regressions were constructed (processed accelerometric variable versus ventilation rate) for each participant and exposure monitor type, and Pearson correlations computed to compare across scenarios.

RESULTS

Triaxial accelerometer data were demonstrated to be adequately sensitive indicators for predicting exposure monitor wearing compliance. Strong linear correlations (R values from 0.77 to 0.99) were observed for all participants for both exposure sensor accelerometer variables against ventilation volume for recumbent, sedentary, and ambulatory activities with MET values ~<6. The RTI monitors mean R value of 0.91 was slightly higher than the Columbia monitors mean of 0.86 due to utilizing a 20 Hz data rate instead of a slower 1 Hz rate. A nominal mean regression slope was computed for the RTI system across participants and showed a modest RSD of +/-36.6%. Comparison of the correlation values of the exposure monitors with the Wocket accelerometers at various body locations showed statistically identical regressions for all sensors at alternate hip, ankle, upper arm, thigh, and pocket locations, but not for the Wocket accelerometer located at the dominant-side wrist location (R=0.57; p=0.016).

CONCLUSIONS

Even with a modest number of adult volunteers, the consistency and linearity of regression slopes for all subjects were very good with excellent within-person Pearson correlations for the accelerometer versus ventilation volume data. Computing accelerometric standard deviations allowed good sensitivity for compliance assessments even for sedentary activities. These pilot findings supported the hypothesis that a common linear regression is likely to be usable for a wider range of adults to predict ventilation volumes from accelerometry data over a range of low to moderate energy level activities. The predicted volumes would then allow real-time estimates of potential dose, enabling more robust panel studies. The poorer correlation in predicting ventilation rate for an accelerometer located on the wrist suggested that this location should not be considered for predictions of ventilation volume.

New approach for particulate exposure monitoring: determination of inhaled particulate mass by 24 h real-time personal exposure monitoring

The objectives of this study were to measure particulate pollution (PM(10), PM(2.5), and PM(1.0)) continuously (24 h/day for 7 day) using real-time exposure monitoring and to estimate total inhalation mass using breathing rate and time-activity. Breathing rates were calculated from measured heart rates. Participants were asked to record a time-activity diary every 15 min. Five microenvironments were defined based on the time-activity diary: home, workplace/school, other indoor, outdoor, and transportation. The average masses of inhaled PM(10) were 530, 316, and 280 μg/day for two office workers, a housewife, and three students, respectively; those of PM(2.5) were 316, 279, and 210 μg/day; and those of PM(1.0) were 251, 264, and 187 μg/day, respectively. We found that home and office/school microenvironments were the main contributors of PM(10), PM(2.5), and PM(1.0) inhaled mass during weekdays and weekends because dwelling time was a determinant factor for inhaled mass. Considering microenvironmental concentration, breathing rate, and dwelling time in each microenvironment, indoor home microenvironments were the largest source of particulate inhalation, followed in order by workplace, transportation, other indoor, and outdoor microenvironments. 34.6% and 69.6% of PM(10) inhalation mass were accumulated in home microenvironments during weekdays and weekends, respectively. The inhaled mass of particulate <1.0 μm (PM(1.0)) in size occupied largest, followed in order by particulate 10-2.5 μm (coarse particle) and 2.5-1.0 μm in size for all occupations.

Home versus nonhome neighborhood: quantifying differences in exposure to the built environment

BACKGROUND

Built environment and health research have focused on characteristics of home neighborhoods, whereas overall environmental exposures occur over larger spatial ranges.

PURPOSE

Differences in built environment characteristics were analyzed for home and nonhome locations using GPS data.

METHODS

GPS data collected in 2007-2008 were analyzed for 41 subjects in the Seattle area in 2010. Environmental characteristics for 3.8 million locations were measured using novel GIS data sets called SmartMaps, representing spatially continuous values of local built environment variables in the domains of neighborhood composition, utilitarian destinations, transportation infrastructure, and traffic conditions. Using bootstrap sampling, CIs were estimated for differences in built environment values for home (<833 m of home address) and nonhome (>1666 m) GPS locations.

RESULTS

Home and nonhome built environment values were significantly different for more than 90% of variables across subjects (p<0.001). Only 51% of subjects had higher counts of supermarkets near than away from home. Different measures of neighborhood parks yielded varying results.

CONCLUSIONS

SmartMaps helped measure local built environment characteristics for a large set of GPS locations. Most subjects had significantly different home and nonhome built environment exposures. Considering the full range of individuals' environmental exposures may improve understanding of effects of the built environment on behavior and health outcomes.

Integrating address geocoding, land use regression, and spatiotemporal geostatistical estimation for groundwater tetrachloroethylene

Geographic information systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend.

High-performance metabolic profiling of plasma from seven mammalian species for simultaneous environmental chemical surveillance and bioeffect monitoring

High-performance metabolic profiling (HPMP) by Fourier-transform mass spectrometry coupled to liquid chromatography gives relative quantification of thousands of chemicals in biologic samples but has had little development for use in toxicology research. In principle, the approach could be useful to detect complex metabolic response patterns to toxicologic exposures and to detect unusual abundances or patterns of potentially toxic chemicals. As an initial study to develop these possible uses, we applied HPMP and bioinformatics analysis to plasma of humans, rhesus macaques, marmosets, pigs, sheep, rats and mice to determine: (1) whether more chemicals are detected in humans living in a less controlled environment than captive species and (2) whether a subset of plasma chemicals with similar inter-species and intra-species variation could be identified for use in comparative toxicology. Results show that the number of chemicals detected was similar in humans (3221) and other species (range 2537-3373). Metabolite patterns were most similar within species and separated samples according to family and order. A total of 1485 chemicals were common to all species; 37% of these matched chemicals in human metabolomic databases and included chemicals in 137 out of 146 human metabolic pathways. Probability-based modularity clustering separated 644 chemicals, including many endogenous metabolites, with inter-species variation similar to intra-species variation. The remaining chemicals had greater inter-species variation and included environmental chemicals as well as GSH and methionine. Together, the data suggest that HPMP provides a platform that can be useful within human populations and controlled animal studies to simultaneously evaluate environmental exposures and biological responses to such exposures.

Human nails analysis as biomarker of exposure to perfluoroalkyl compounds

Extensive human exposure to perfluoroalkyl compounds (PFAA) together with their persistence and various toxicities have arisen increasing concern. A noninvasive method would improve exposure assessment for large population, especially the children susceptible to contaminants. The aim of the study was to assess the use of PFAA measurements in human nails as a biomarker of exposure to PFAAs. Fingernail, toenail, and blood samples were collected from 28 volunteers. The PFAA concentrations were determined by high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Six PFAA were detected in nails, with perfluorooctane sulfonate (PFOS) being the compound with the highest median concentration (33.5 and 26.1 ng/g in fingernail and toenail, respectively). Followed was perfluorononanoate (PFNA), with the median concentrations of 20.4 and 16.8 ng/g, respectively, in fingernail and toenail. Other PFAA detected were perfluorooctanoate (PFOA), perfluorodecanoate (PFDA), perfluorododecanoate (PFDoA), and perfluorotetradecanoate (PFTA), with median levels ranging between 0.19 and 8.94 ng/g. PFOS and PFNA concentrations in fingernail significantly correlated with those in serum. Fingernail PFOS and PFNA levels were 2.8 and 24.4 times, respectively, higher than the serum levels. The accumulation of PFAA in nails, together with its advantages in noninvasive sampling and ability of reflecting long-term exposure, made nails PFAA an attractive biomarker of exposure.

Assessment of non-occupational exposure to polycyclic aromatic hydrocarbons through personal air sampling and urinary biomonitoring

Non-occupational inhalation and ingestion exposure to polycyclic aromatic hydrocarbons (PAHs) has been studied in 8 non-smoking volunteers through personal air sampling and urinary biomonitoring. The study period was divided into 4 segments (2 days/segment), including weekdays with regular commute and weekends with limited traffic related exposures; each segment had a high or low PAH diet. Personal air samples were collected continuously from the subjects while at home, at work, and while commuting to and from work. All urine excretions were collected as individual samples during the study. In personal air samples, 28 PAHs were measured, and in urine samples 9 mono-hydroxylated metabolites (OH-PAHs) from 4 parent PAHs (naphthalene, fluorene, phenanthrene and pyrene) were measured. Naphthalene was found at higher concentrations in air samples collected at the subjects' residences, whereas PAHs with four or more aromatic rings were found at higher levels in samples taken while commuting. Urinary OH-PAH biomarker levels increased following reported high inhalation and/or dietary exposure. On days with a low PAH diet, the total amount of inhaled naphthalene during each 24-hour period was well correlated with the amount of excreted naphthols, as was, to a lesser extent, fluorene with its urinary metabolites. During days with a high dietary intake, only naphthalene was significantly correlated with its excreted metabolite. These findings suggest that this group of non-occupational subjects were exposed to naphthalene primarily through indoor air inhalation, and exposed to other PAHs such as pyrene mainly through ingestion.

Exploration of the rapid effects of personal fine particulate matter exposure on arterial hemodynamics and vascular function during the same day

BACKGROUND

Levels of fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM(2.5))] are associated with alterations in arterial hemodynamics and vascular function. However, the characteristics of the same-day exposure-response relationships remain unclear.

OBJECTIVES

We aimed to explore the effects of personal PM(2.5) exposures within the preceding 24 hr on blood pressure (BP), heart rate (HR), brachial artery diameter (BAD), endothelial function [flow-mediated dilatation (FMD)], and nitroglycerin-mediated dilatation (NMD).

METHODS

Fifty-one nonsmoking subjects had up to 5 consecutive days of 24-hr personal PM(2.5) monitoring and daily cardiovascular (CV) measurements during summer and/or winter periods. The associations between integrated hour-long total personal PM(2.5) exposure (TPE) levels (continuous nephelometry among compliant subjects with low secondhand tobacco smoke exposures; n = 30) with the CV outcomes were assessed over a 24-hr period by linear mixed models.

RESULTS

We observed the strongest associations (and smallest estimation errors) between HR and TPE recorded 1-10 hr before CV measurements. The associations were not pronounced for the other time lags (11-24 hr). The associations between TPE and FMD or BAD did not show as clear a temporal pattern. However, we found some suggestion of a negative association with FMD and a positive association with BAD related to TPE just before measurement (0-2 hr).

CONCLUSIONS

Brief elevations in ambient TPE levels encountered during routine daily activity were associated with small increases in HR and trends toward conduit arterial vasodilatation and endothelial dysfunction within a few hours of exposure. These responses could reflect acute PM(2.5)-induced autonomic imbalance and may factor in the associated rapid increase in CV risk among susceptible individuals.

Leveraging Epidemiology to Improve Risk Assessment

The field of environmental public health is at an important crossroad. Our current biomonitoring efforts document widespread exposure to a host of chemicals for which toxicity information is lacking. At the same time, advances in the fields of genomics, proteomics, metabolomics, genetics and epigenetics are yielding volumes of data at a rapid pace. Our ability to detect chemicals in biological and environmental media has far outpaced our ability to interpret their health relevance, and as a result, the environmental risk paradigm, in its current state, is antiquated and ill-equipped to make the best use of these new data. In light of new scientific developments and the pressing need to characterize the public health burdens of chemicals, it is imperative to reinvigorate the use of environmental epidemiology in chemical risk assessment. Two case studies of chemical assessments from the Environmental Protection Agency Integrated Risk Information System database are presented to illustrate opportunities where epidemiologic data could have been used in place of experimental animal data in dose-response assessment, or where different approaches, techniques, or studies could have been employed to better utilize existing epidemiologic evidence. Based on the case studies and what can be learned from recent scientific advances and improved approaches to utilizing human data for dose-response estimation, recommendations are provided for the disciplines of epidemiology and risk assessment for enhancing the role of epidemiologic data in hazard identification and dose-response assessment.

Comparing household listing techniques in a rural midwestern Vanguard Center of the National Children's Study

The National Children's Study (NCS) is a longitudinal study that will examine the influence of environmental and social factors on the health and development of 100,000 children, following them from before birth until age 21. Proposed participant recruitment methods call for locating and listing all dwelling units (DUs) located within randomly selected segments within the 105 NCS sites. One of seven Vanguard Centers of the NCS includes four rural counties that span approximately 2,500 square miles. The size of this sampling area presents unique geographic challenges. In order to determine the most efficient method for listing DUs within this large area, a study was undertaken to investigate the differences in the percent of DUs identified and the cost of four different approaches. It compared the on-site listing method of physically identifying each DU with three other methods: plat maps, postal listings, and Geographic Information Systems (GIS)/satellite imagery techniques. The on-site method had the strongest kappa (.85) in terms of identifying true DUs. There was a moderate agreement (.59) with the plat map method, fair agreement (.34) with the postal method, and only a slight agreement (.14) with the GIS/satellite imagery method. The plat map, postal listing, and GIS/satellite methods were less time-consuming than the on-site method.

Exposure science: a view of the past and milestones for the future

BACKGROUND

The study of human exposure to environmental toxicants has evolved as a scientific field over the past 30 years.

OBJECTIVES

This review provides a historical perspective on the growth of exposure science as a field, with some emphasis on the results from initial observational studies in obtaining information needed for generating hypotheses on significant human contact with environmental agents, testing the performance of models, and reducing exposures to protect public health.

DISCUSSION

Advances in activity pattern and behavioral research that established a suite of variables needed to accurately define contact and factors that influence contact are also discussed. The identification and characterization of these factors have played a pivotal role in the growth of the field and in developing exposure reduction strategies. Answers to two key questions on the relevance and fundamental value of exposure science to the fields of environmental health and risk management are presented as a path forward: a) What does one do with such exposure information? b) What roles does exposure science play in situations beyond observational analyses and interpretation?

CONCLUSIONS

The discussion identifies the need for more focused use of observational studies of exposure for epidemiologic analyses. Further, the introduction and use of new tools and approaches for hypothesis testing that can improve the use of exposure science in prevention research for risk management is needed to affect the source-to-effect continuum. A major restructuring of the field is not required to achieve innovation. However, additional resources for training and education are required to ensure that the potential for exposure science to play a central role in reducing and preventing excess risk within environmental/occupational health is achieved.

How should environmental exposure risk be assessed? A comparison of four methods for exposure assessment of air pollutions

Methods commonly used to assess the environmental exposure risk at a location (e.g., proximity models) are usually based on different assumptions, leading to conflicting results and recommendations in epidemiological studies. In this case study, a comparative evaluation of the accuracy levels associated with four commonly used exposure risk estimate models [i.e., traditional proximity model (TPM), emission weighted proximity model (EWPM), the American Meteorological Society/EPA regulatory model (AERMOD), and ordinary kriging interpolation (OKI)] were conducted. Results show that at the annual and the monthly scales, the normalized exposure risk values simulated by AERMOD and EWPM have higher accuracy levels than the simulations from the TPM and OKI methods. However, AERMOD has higher accuracy than that of the EWPM, and this was attributed to the differences of input data. EWPM provided the most accurate simulations when analysts have access to only point emission source data. The results also indicate that the accuracies of the exposure risks simulated by AERMOD and EWPM can be influenced by factors such as the modeling extent, the distance settings, and so forth.

Overview of the symposium on public health significance of genomics and eco-genetics

Genomic and genetic information is rapidly becoming a major element in public health research and emerging public health practice. This symposium reviews the methods, findings, and significance of genome-wide association studies from epidemiological and statistical points of view. We examine infectious and inflammatory components of gene-environment interaction in the respiratory system. We note the need for nutrient and dietary data and many other kinds of environmental exposure data in population-based genomic studies. Then we explore the sufficiency of a well-informed family history for public health and family counseling purposes. Finally, in an era of direct-to-consumer genomic test promotion, we review the evidence on the critical question, will genetic risk profiles motivate individuals and families to choose more healthful behaviors? This symposium builds on the foundation of the symposium on Public Health Genetics in Volume 21 (2000) of the Annual Review of Public Health.

Exposure science and the U.S. EPA National Center for Computational Toxicology

The emerging field of computational toxicology applies mathematical and computer models and molecular biological and chemical approaches to explore both qualitative and quantitative relationships between sources of environmental pollutant exposure and adverse health outcomes. The integration of modern computing with molecular biology and chemistry will allow scientists to better prioritize data, inform decision makers on chemical risk assessments and understand a chemical's progression from the environment to the target tissue within an organism and ultimately to the key steps that trigger an adverse health effect. In this paper, several of the major research activities being sponsored by Environmental Protection Agency's National Center for Computational Toxicology are highlighted. Potential links between research in computational toxicology and human exposure science are identified. As with the traditional approaches for toxicity testing and hazard assessment, exposure science is required to inform design and interpretation of high-throughput assays. In addition, common themes inherent throughout National Center for Computational Toxicology research activities are highlighted for emphasis as exposure science advances into the 21st century.

Assessing and maximizing the acceptability of global positioning system device use for studying the role of human movement in dengue virus transmission in Iquitos, Peru

As use of global positioning system (GPS) technology to study disease transmission increases, it is important to assess possible barriers to its use from the perspective of potential study participants. Fifteen focus group discussions stratified by sex, age, and motherhood status were conducted in 2008 in Iquitos, Peru. All participants said they would accept using a GPS unit for study purposes for 2-4 weeks. Participants' main concerns included caring properly for the unit, whether the unit would audio/videotape them, health effects of prolonged use, responsibility for units, and confidentiality of information. A pilot study was then conducted in which 126 persons were asked to carry GPS units for 2-4 weeks; 98% provided consent. All persons used the units expressing minimal concerns, although 44% reported forgetting the device at least once. Our study is the first to highlight participant concerns related to use of GPS for long-term monitoring of individual behavior in a resource-limited setting.