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Journal of Occupational & Environmental Medicine:
doi: 10.1097/JOM.0b013e31821bde98
Epidemiologic Research: Original Article

Epidemiologic Challenges for Studies of Occupational Exposure to Engineered Nanoparticles; A Commentary

Eisen, Ellen A. ScD; Costello, Sadie PhD; Chevrier, Jonathan PhD; Picciotto, Sally PhD

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From the Environmental Health Sciences, School of Public Health, University of California Berkeley.

Address correspondence to: Ellen A. Eisen, ScD, Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California 94720-7360. E-mail: eeisen@berkeley.edu.

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Abstract

Objective: Identify most likely health effects of occupational exposure to engineered nanoparticles (ENP). Recommend analytic approaches to address epidemiologic challenges.

Methods: Review air pollution and occupational literature on health effects of fine particulate matter (PM). Provide example of mortality study of exposure to PM composed of metalworking fluid. Apply standard Cox models and g-estimation to adjust for potential healthy worker survival effect (HWSE).

Results: In contrast with standard methods, g-estimation suggests that exposure to PM may cause chronic heart and lung disease; longer exposure reduces survival. HWSE appears stronger for chronic disease than for cancer.

Conclusions: We recommend hazard surveillance, short-term panel studies of biomarkers, and prospective cohort studies of cardiovascular and respiratory diseases. Building research capacity in g-estimation methods to reduce HWSE is necessary for future studies of chronic disease and ENP.

Many complex and unresolved issues related to the characterization of engineered nanoparticles (ENP) need to be confronted in the planning of an epidemiologic study. Here we propose three recommendations intended to address the major challenges to designing epidemiologic studies of occupational exposure to ENP.

First, there is the question of specifying the exposures of interest. To date, nanotoxicology has focused on a limited number of engineered nanomaterials including carbon nanotubes, but there are many types of nanomaterials. Second, the relationships between physiochemical properties and bioactivity are not yet well understood, leaving an open question about the most biologically relevant exposure metrics of these particles. Third, study populations of workers employed in the manufacture or use of specific classes of nanoparticles need to be characterized. Hazard surveillance offers a framework for the systematic collection and analysis of exposure data to resolve these issues and provide a basis for future health studies.

Recommendation 1: Implement hazard surveillance of a broad range of ENP with regular monitoring of both number concentrations and mass concentrations to identify exposed cohorts and to document job and exposure histories for future studies.

Anticipating the most likely adverse health effects presents another challenge. In the absence of any human data, it is reasonable to turn to known health effects of similar exposures in the ambient or occupational environment. We begin by reviewing the extensive literature on health effects of fine particulate matter (PM2.5) and ultrafine particles (UFP) in urban air pollution. (UFP and nanoparticles are used synonymously to describe particles less than 0.1 μm in aerodynamic diameter.) We then review what we know about those same health effects in relation to workplace exposures similar in particle size distribution and composition to urban traffic PM. Although the novel physiochemical properties of ENP may cause new mechanisms of injury, studies of workers exposed to PM2.5 with a high UFP component (boilermakers, welders, and autoworkers) provide a reasonable basis for identifying the most likely health effects of ENP.

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LEARNING FROM AIR POLLUTION STUDIES OF FINE AND ULTRAFINE PARTICLES (NANOPARTICLES)

Cardiovascular disease (CVD) was first associated with air pollution in a mortality study of the Six Cities cohort initially designed to assess pulmonary function. Comparing the most polluted to the least polluted of the six US cities, Dockery et al1 reported an adjusted mortality rate ratio for CVD of 1.26 (95% confidence interval [CI] = 1.08 to 1.47). Since that time, a wide and compelling literature has evolved on the basis of hospital admissions2,3 and mortality,1,48 establishing that exposure to ambient air pollution increases the risk of CVD. Mounting evidence suggests that the primary cause of this increased risk is PM—especially PM2.5 generated from combustion sources, that is, urban traffic.9 Attention is now shifting to the smaller UFP in traffic-derived pollution; recent studies of daily cardiopulmonary mortality10 and biomarkers of platelet activation11 suggest that UFP (PM0.1) may be more toxic than PM2.5. It is the UFP fraction of traffic emission that appears to contain most of the polycyclic aromatic hydrocarbons (PAHs), a carcinogenic component of oil that is also generated by combustion.12 Potential pathways have been identified to explain how exposure to UFP in traffic pollution may cause CVD.13 The association between outdoor air pollutants and exacerbation of preexisting chronic obstructive pulmonary disease (COPD) is also supported by reasonable evidence; ambient PM has been linked with hospital admissions2,14 and emergency department visits for respiratory disease3 and COPD,15 as well as with COPD mortality.16 Extrapolating from the ambient environment, the most likely health outcomes of exposure to ENP are chronic heart and lung diseases.

Daily exposure has been linked to CVD and COPD hospitalizations and mortality in numerous time-series studies.17 Attention has now shifted to studies of long-term exposure to air pollution, defined as 1 year or more.18 Concerns about potential confounding by sociodemographic characteristics have led to the development of sophisticated graphical methods for characterizing small-scale spatial gradients in urban air pollution. Less attention has been devoted to measuring changes in the concentrations of ambient exposures over time at the individual level, although age is a convenient measure of duration of exposure.

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OCCUPATIONAL STUDIES OF RESPIRABLE AND ULTRAFINE PARTICLES

By contrast, exposure to workplace hazards can generally be well characterized by combining employment records with industrial hygiene sampling data. UFP, however, have not been regularly monitored in the workplace. Although most mechanical processes that generate dusty occupational environments are unlikely to produce significant number concentrations of UFP, hot processes that involve vaporization and inevitable cooling may.19 A cohort of Norwegian asphalt production workers and pavers was reported to be exposed to UFP, at a concentration of 3.4 × 104/cm3, as well as to mineral oils and PAHs.20 In addition to such hot processes as asphalt paving, UFP can also originate from combustion, for example in diesel engines, and high-speed mechanical processes such as grinding.21 In a recent exposure survey of seven Swedish industries, UFP was measured by number concentration and surface area in combustion processes such as diesel engines, hot processes such as welding and smelting (30 × 103 to 100 × 103 n/cm), and high-speed grinding (10 × 103 n/cm).21

There are no studies of the potential health effects of UFP in the workplace, and the literature on occupational exposures to respirable PM3.5 (or fine PM2.5) is limited to short-term studies of acute effects. In recent studies of boilermakers, biomarkers of preclinical cardiovascular effects, including markers of inflammation22,23 and heart-rate variability24 have been associated with exposure to metal-rich PM2.5 containing PAHs. By design, these were panel studies with multiple measures of outcome and exposure collected over several days. Biomarkers of cardiovascular effects were measured frequently, simultaneous with continuous PM2.5 exposure monitoring in a small number of subjects (typically fewer than 30). Because subjects can be compared with themselves, confounding can only occur by factors that vary over time. Thus, this is an efficient design for studying physiologic pathways of biologic effects.

Recommendation 2: Study biomarkers of short-term cardiovascular and pulmonary responses in small panel studies of workers exposed to specific types of ENP.

By observing repeated measures of biomarker outcomes at regular intervals and monitoring real-time exposure, even studies with small sample size can have sufficient statistical power to detect small health effects. This design will allow alternative exposure metrics of ENP to be examined in relation to outcomes and provide new insights on disease mechanisms.

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STUDIES OF CHRONIC DISEASE IN WORKER COHORTS EXPOSED TO UFP

The occupational literature on both CVD and COPD incidences or mortalities is limited and surprisingly inconclusive, given the magnitude of workplace exposure to PM and PAHs.25,26 There are a few studies, however, that suggest that workers are also at risk of chronic disease. In a large cohort of European asphalt pavers, a 60% increase in fatal myocardial infarctions was associated with an average exposure of 273 ng/m3 benzo(a)pyrene, a specific PAH, relative to in those unexposed.27 In a cohort of Canadian aluminum smelter workers, an elevated hazard ratio (HR) for ischemic heart disease (IHD) was found in the highest categories of both past and current exposures to benzo(a)pyrene, a marker of coal tar pitch volatiles, among the actively employed.28 Workplace PM exposure is currently regulated under a generic standard for particles not otherwise classified, at 5 and 15 mg/m3 for respirable and total particulate matter, respectively, as an 8-hour daily time-weighted average.29 Even after adjusting for differences in the length of a day, permissible exposure limits in occupational settings are up to three orders of magnitude higher than that allowed in the general community.30,31

In a recent study based on the American Cancer Society cohort of more than 1 million US adults, Pope and colleagues32 analyzed data on cardiovascular mortality, ambient PM2.5, and both active and secondhand cigarette smoke by using a common daily exposure metric. Results suggest a log linear exposure–response relationship with excess risk even at low exposure levels. The occupational exposures to respirable PM or fine PM2.5 in many US manufacturing plants lie in the exposure gap between ambient air pollution and active smoking. Filling in the missing range with adjusted HRs for cardiovascular mortality and daily exposure to fine PM2.5 in occupational settings will potentially identify new worker populations at risk. Moreover, studying chronic disease in worker cohorts exposed to fine or respirable PM (including UFP) will be relevant for planning future health studies of ENP.

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CHRONIC HEART AND LUNG DISEASE AND PM EXPOSURE IN OCCUPATIONAL SETTINGS

To highlight the challenges posed by studying chronic disease in occupational cohorts, new results are presented for a cohort of United Autoworkers-General Motors (UAW-GM) workers exposed to PM composed of metalworking fluids (MWF). This cohort has been observed for mortality from 1941 to 1995.33 An extensive retrospective exposure assessment for oil-based MWF was conducted on the basis of size-selective gravimetric sampling data. The (unmeasured) PAH content of straight MWF has declined since the 1980s but may still be present.34 The PM generated when straight MWF are sprayed to cool machining and grinding operations contains a high proportion of respirable particles. Although UFP have not been measured, the rapid heating and cooling of mineral oils potentially generates significant number concentrations of UFP.19 Long-term personal exposures have been estimated by combining employment records with historical exposure monitoring data. Figure 1 presents the average annual concentration (mg/m3) of PM composed of straight MWF, by particle size, in one of the three automobile manufacturing plants in the UAW-GM study. The graph shows that PM3.5 accounts for about 30% of average annual concentration of total PM, across all jobs in each year over the study period.

Figure 1
Figure 1
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To date, several cancers have been associated with straight MWF on the basis of internal analysis of quantitative estimates of past exposure.33,3541 We have also reported standardized mortality ratios for COPD mortality of 0.94 (95% CI = 0.87 to 1.00) for all white male population and 0.78 (0.64 to 0.95) for all African American male population in the cohort compared with the general US population.33 Here we present new exposure–response results, based on 2659 deaths due to IHD (Fig. 2) and 306 deaths due to COPD (Fig. 3). HRs for IHD and COPD mortality were modeled as smoothed functions of cumulative exposure to straight MWF (total PM mg/m3-years). Smoothing was implemented using penalized splines in Cox models adjusted for gender, race, plant, and calendar year, with age as the time metric. For CVD, the exposure–response curve suggests a modest rise in relative risk over the densest portion of the exposure range, with a plateau in the relative risk at HR equal to 1.2. For COPD, the exposure–response increases across the entire range. For both outcomes, the confidence bands are wide and include the null.

Figure 2
Figure 2
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Figure 3
Figure 3
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One might expect more conclusive results, given the range of PM exposures in the autoworkers study. There are several possible explanations for why the CVD curve plateaus and the confidence bands are so wide, but two are most relevant here. First, the exposure–response models were based on cumulative exposure to total PM; the air pollution literature has shown that the smaller particles have greater cardiovascular toxicity. Thus the exposure–response curve for PM3.5 may be steeper and (or) have tighter CIs. The second explanation is the healthy worker survivor effect (HWSE).

It is important to keep in mind that associations in occupational studies are attenuated because of HWSE.42 Downward bias arises when less healthy workers reduce exposure by transferring jobs, taking time off work, or terminating employment, leaving healthier workers with more exposure. It is plausible that HWSE impacts chronic diseases-–with long survival and a lot of symptoms–more than more rapidly fatal diseases such as cancer. We recently applied g-estimation to address HWSE in the autoworkers cohort.43,44

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G-ESTIMATION TO REDUCE HWSE BIAS

Causal methods contrast outcomes that would have been observed in scenarios where exchangeable (or the same) individuals are subjected to different levels of exposure. Robins45 has shown that standard conditional epidemiologic models (Cox, logistic, and Poisson regression) will be biased if past exposure predicts future values of a time-dependent variable, which is both a risk factor for survival and predicts subsequent exposure. In such situations, causal models are needed to provide unbiased dose–response estimates. Health status is such a time-dependent variable, so we need causal models to avoid bias due to HWSE.45,46 Most causal methods other than g-estimation of accelerated failure time models require that all levels of exposure occur in all strata of the confounders. In occupational studies, however, those not actively employed are unexposed by definition. Thus, g-estimation is a causal approach that can be applied to adjust for HWSE in the occupational setting. Although causal models are becoming part of the mainstream epidemiologic literature,47 they had not been applied at all in occupational studies until recently.

We have applied g-estimation to the autoworkers cohort study as reported by Chevrier and Eisen.43,44 The results are briefly summarized here and extended (J.C., S.P., E.A.E., unpublished data, September 2010). The autoworker cohort was restricted to 38,747 subjects hired after start of follow-up. A total of 2595 subjects died of IHD (International Classification of Diseases, Ninth Revision: 410 to 414) over the follow-up period. Exposure to straight MWF was treated as a binary variable (ever vs never exposed) in each year and the g-estimated survival ratio (SR) compares survival if everyone had been exposed for the first 5 years with survival if no one had ever been exposed. We used the results from g-estimation to calculate survival curves for IHD mortality under alternative exposure histories. The solid line in Fig. 4 represents observed survival, where each worker's actual exposure is included. The other three curves are hypothetical, based on our g-estimation analysis. The dotted line represents survival that would have been observed if no one had been exposed at all, the dashed line represents survival that would have been observed if everyone had been exposed for the first 5 years of follow-up, and the dash-dotted line represents survival that would have been observed if everyone had been always exposed. Because exposure can cause IHD, the curve representing survival if no one were exposed shows greater survival at each time than under any other scenario (including the observed); and survival if always exposed is less than under any other scenario. The curves indicate that longer exposure is more harmful, and that long follow-up is required to see the effect of MWF on IHD mortality.

Figure 4
Figure 4
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As described in Chevrier and Eisen,43,44 the SRs were transformed into HRs so that we could compare g-estimation results with standard methods. Adjusted HRs estimated in standard Cox models (with time on follow-up as the metameter) were 0.97 (95% CI = 0.94 to 1.00) for both IHD and all cancers combined per 5 years of exposure, and 0.99 (95% CI = 0.91 to 1.07) for COPD. The g-estimated HRs were higher for all three outcomes. The application of g-estimation reversed the direction of the HR for each outcome, from below the null to an elevated HR with a CI that excluded the null. The two methods differ in several respects; however, the difference between the g-estimated and standard HR for each outcome may provide some information about the magnitude of HSWE bias. The differences, expressed as a percentage of the g-estimate, were 22% for IHD, 33% for COPD, and 13% for all cancers combined. These results suggest that the downward HWSE bias may be stronger for heart disease and chronic lung disease than for cancer.

Early studies of ENP exposure should focus on cardiovascular and respiratory diseases incidence rather than mortality. Occupational mortality studies, however, are retrospective studies and require many years of follow-up. By contrast, incidence studies can be prospective, based on hospital discharge data or data collected on biomarkers of subclinical arteriosclerosis, such as carotid wall intima-medial thickness,18 for example. Past-exposure information needed for these chronic disease studies at the individual level may be available from the hazard surveillance recommended earlier. In any occupational study of chronic disease, whether prospective or retrospective, HWSE is a challenge to study validity and must be addressed.

Recommendation 3: Plan prospective studies of CVD and COPD incidences in relation to occupational exposure to ENP, and make sure to measure a time-varying health status variable (eg, time off work) so that g-estimation can be applied to address downward bias.

To anticipate challenges of studying worker cohorts exposed to ENP, we considered studies of workers exposed to small particles. In the absence of any epidemiologic studies of occupational exposure to UFP, we turned to our own studies of respirable PM with PAH. The relevance of the challenges identified in the autoworkers study to the future study of ENP is speculative. ENP may cause health effects other than the respiratory and cardiovascular effects related to combustion-generated PM. Our experience in occupational studies of chronic disease-–whether retrospective or prospective—highlights the importance of taking account of healthy worker survivor bias in study design.

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