Policy implications of genetic information on regulation under the Clean Air Act: the case of particulate matter and asthmatics

Antioxidant genes and susceptibility to air pollution for respiratory and cardiovascular health

Oxidative stress occurs when antioxidant defences, which are regulated by a complex network of genes, are insufficient to maintain the level of reactive oxygen species below a toxic threshold. Outdoor air pollution has long been known to adversely affect health and one prominent mechanism of action common to all pollutants is the induction of oxidative stress. An individual's susceptibility to the effects of air pollution partly depends on variation in their antioxidant genes. Thus, understanding antioxidant gene-pollution interactions has significant potential clinical and public health impacts, including the development of targeted and cost-effective preventive measures, such as setting appropriate standards which protect all members of the population. In this review, we aimed to summarize the latest epidemiological evidence on interactions between antioxidant genes and outdoor air pollution, in the context of respiratory and cardiovascular health. The evidence supporting the existence of interactions between antioxidant genes and outdoor air pollution is strongest for childhood asthma and wheeze, especially for interactions with GSTT1, GSTM1 and GSTP1, for lung function in both children and adults for several antioxidant genes (GSTT1, GSTM1, GSTP1, HMOX1, NQO1, and SOD2) and, to a more limited extent, for heart rate variability in adults for GSTM1 and HMOX1. Methodological challenges hampering a clear interpretation of these findings and understanding of true potential heterogeneity are discussed.

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.

Genetics and asthma disease susceptibility in the US Latino population

The US Latino population is heterogeneous with diversity in environmental exposures and socioeconomic status. Moreover, the US Hispanic population derives from numerous countries previously under Spanish rule, and many Hispanics have complex proportions of European, Native American, and African ancestry. Disparities in asthma severity and control are due to complex interactions between environmental exposures, socioeconomic factors, and genetic variations. In addition, diseases within the Latino community may also differ by country of origin. Although US Census data show low asthma rates in the Hispanic population as a whole, there is a lot of variability in the prevalence and morbidity of asthma, with a prevalence of 5.0% in Mexican Americans versus 17.0% in Puerto Ricans. The diversity and population admixture make the study of the genetics of asthma complex in Latino populations. However, an understanding of the genetics of asthma in all populations, including the Latino population, can enhance risk identification, help us to target pharmacological therapy, and guide environmental regulations, all of which can promote a reduction in health disparities. The inclusion of markers of ancestral diversity and the incorporation of techniques to adjust for stratification now make these studies feasible in complex populations, including the Latino population. To date, studies using linkage analyses, genome-wide associations, or candidate gene analyses have identified an association of asthma or asthma-related phenotypes with candidate genes, including interleukin 13, beta-2 adrenergic receptor, a disintegrin and metalloproteinase 33, orosomucoid 1-like 3, and thymic stromal lymphopoietin. As reviewed here, although these genes have been identified in diverse populations, limited studies have been performed in Latino populations, and they have had variable replication. There is a need for the development of registries with well-phenotyped pediatric and adult Latino populations and subgroups for inclusion in the rapidly expanding field of genetic studies, and these studies need to be used to reduce health disparities.

Is the air pollution health research community prepared to support a multipollutant air quality management framework?

Ambient air pollution is always encountered as a complex mixture, but past regulatory and research strategies largely focused on single pollutants, pollutant classes, and sources one-at-a-time. There is a trend toward managing air quality in a progressively "multipollutant" manner, with the idealized goal of controlling as many air contaminants as possible in an integrated manner to achieve the greatest total reduction of adverse health and environmental impacts. This commentary considers the current ability of the environmental air pollution exposure and health research communities to provide evidence to inform the development of multipollutant air quality management strategies and assess their effectiveness. The commentary is not a literature review, but a summary of key issues and information gaps, strategies for filling the gaps, and realistic expectations for progress that could be made during the next decade. The greatest need is for researchers and sponsors to address air quality health impacts from a truly multipollutant perspective, and the most limiting current information gap is knowledge of personal exposures of different subpopulations, considering activities and microenvironments. Emphasis is needed on clarifying the roles of a broader range of pollutants and their combinations in a more forward-looking manner; that is not driven by current regulatory structures. Although advances in research tools and outcome data will enhance progress, the greater need is to direct existing capabilities toward strategies aimed at placing into proper context the contributions of multiple pollutants and their combinations to the health burdens, and the relative contributions of pollutants and other factors influencing the same outcomes. The authors conclude that the research community has very limited ability to advise multipollutant air quality management and assess its effectiveness at this time, but that considerable progress can be made in a decade, even at current funding levels, if resources and incentives are shifted appropriately.

The application of genetic information for regulatory standard setting under the clean air act: a decision-analytic approach

In 2002, the U.S. Environmental Protection Agency (EPA) released an "Interim Policy on Genomics," stating a commitment to developing guidance on the inclusion of genetic information in regulatory decision making. This statement was followed in 2004 by a document exploring the potential implications. Genetic information can play a key role in understanding and quantifying human susceptibility, an essential step in many of the risk assessments used to shape policy. For example, the federal Clean Air Act (CAA) requires EPA to set National Ambient Air Quality Standards (NAAQS) for criteria pollutants at levels to protect even sensitive populations from adverse health effects with an adequate margin of safety. Asthmatics are generally regarded as a sensitive population, yet substantial research gaps in understanding genetic susceptibility and disease have hindered quantitative risk analysis. This case study assesses the potential role of genomic information regarding susceptible populations in the NAAQS process for fine particulate matter (PM(2.5)) under the CAA. In this initial assessment, we model the contribution of a single polymorphism to asthma risk and mortality risk; however, multiple polymorphisms and interactions (gene-gene and gene-environment) are known to play key roles in the disease process. We show that the impact of new information about susceptibility on estimates of population risk or average risk derived from large epidemiological studies depends on the circumstances. We also suggest that analysis of a single polymorphism, or other risk factor such as health status, may or may not change estimates of individual risk enough to alter a particular regulatory decision, but this depends on specific characteristics of the decision and risk information. We also show how new information about susceptibility in the context of the NAAQS for PM(2.5) could have a large impact on the estimated distribution of individual risk. This would occur if a group were consequently identified (based on genetic and/or disease status), that accounted for a disproportionate share of observed effects. Our results highlight certain conditions under which genetic information is likely to have an impact on risk estimates and the balance of costs and benefits within groups, and highlight critical research needs. As future studies explore more fully the relationship between exposure, genetic makeup, and disease status, the opportunity for genetic information and disease status to play pivotal roles in regulation can only increase.