Occupational exposure to diesel engine exhaust and alterations in lymphocyte subsets

ABSTRACT

Background 

The International Agency for Research on Cancer recently classified diesel engine exhaust (DEE) as a Group I carcinogen based largely on its association with lung cancer. However, the exposure-response relationship is still a subject of debate and the underlying mechanism by which DEE causes lung cancer in humans is not well understood.

Methods 

We conducted a cross-sectional molecular epidemiology study in a diesel engine truck testing facility of 54 workers exposed to a wide range of DEE (ie, elemental carbon air levels, median range: 49.7, 6.1-107.7 mg/m3) and 55 unexposed comparable controls.

Results 

The total lymphocyte count (p=0.00044) and three of the four major lymphocyte subsets (ie, CD4+
T cells (p=0.00019), CD8+ T cells (p=0.0058) and B cells ( p=0.017)) were higher in exposed versus control workers and findings were highly consistent when stratified by smoking status. In addition, there was evidence of an exposure-response relationship between elemental carbon and these end points (ptrends<0.05), and CD4+ T cell levels were significantly higher in the lowest tertile of DEE exposed workers compared to controls (p=0.012).

Conclusions 

Our results suggest that DEE exposure is associated with higher levels of cells that play a key role in the inflammatory process, which is increasingly being recognised as contributing to the aetiology of lung cancer.

Impact

This study provides new insights into the underlying mechanism of DEE carcinogenicity

MATERIALS AND METHODS

Diesel engine facility

Exposed workers were selected from an engine testing facility of a diesel engine manufacturing company that produces diesel engines for light and heavy trucks (production between 10 000 and 20 000 engines a month). All engines, after assembly and without any after treatment system, are tested and tuned by placing them on a diesel engine dynamometer in a ‘Dyno-room’. The facility has in total 44 Dyno-rooms that are semienclosed (open at the top); on average, 25–50% are in use at any point in time. Engine exhaust is ventilated to the outside by directly coupling the engine exhaust to a local exhaust system, and by general ventilation of the Dyno-rooms.

Control factories

Control workplaces were selected based on the absence of DEE and general dust exposure. Selected facilities included a bottling department of a brewery (n=24), a water treatment plant (n=18), a meat packing facility (n=8) and an administrative facility (n=5). Based on detailed walk-through surveys, no DEE sources were identified in any of these workplaces.

Exposure assessment

The exposure assessment survey in the diesel factory was con- ducted from October 2012 to March 2013 encompassing all workers in the testing facility. Repeated full-shift personal air samples for elemental and organic carbon (OC), and fine particulate matter were collected using a cyclone attached to the lapel near the breathing zone, with an aerodynamic cut-off of 2.5 mm (PM2.5) at a ﬂow rate of 3.5 L/min using quartz or Teflon filters, respectively. PM2.5 was assessed by preweighing and postweighing of the Teflon filters in an environmentally controlled weighing room using a microbalance at 1 mg accuracy. Each filter was preweighed and postweighed in duplicate. If the duplicate measurements differed by more than 5 mg, the filter was reweighed. Elemental carbon (EC) and OC were measured on the quartz filters using NIOSH Method 5040.6 Weights (PM2.5, EC and OC) were divided by the volume of air drawn through the filters to provide exposure concentrations (mg/m3). We determined soot content of the Teﬂon and quartz filters using a smoke stain reflectometer (model M43D, diffusion Systems Ltd, London, UK) and converted the reflectance value into an absorption coefficient (10−5/m).

Normal probability plots indicated that PM2.5, EC, OC and soot values followed essentially a log-normal distribution. Individual exposure was estimated using a mixed effect model using the natural logarithm of PM2.5 (n=71), EC (n=149), OC (n=149) and soot (n=237). Subjects were assigned as random effects. Fixed effects included job title based on position/task of the subjects in the testing department (testing workers and their location within the department, refuelling workers and inspection workers) and the season during which the measurement was taken (Autumn, Winter, Spring). Location of testing workers was included in the model as general ventilation efficiency differed within the testing department. For EC and OC, we additionally included a fixed effect for current smoking status of the subject, as smoking was allowed during breaks at the workplace and influenced these particular exposure proxies. For soot we additionally included a parameter for the filter type, as reflction was slightly different depending on the type of filter used (ie, Teﬂon, quartz).

The following formula was used to summarise the model: where yij represents the natural log transformed value of the PM2.5, EC, OC, or soot exposure levels for person i, on day j. μ represents the intercept (ie, the ‘background’ level). β1 through βn represent fixed effect variable coeeffcients for variables x1 through xn. bIi represents the random effect coefficient for subject i. εij represents the error for subject i, on day j. Individual year average exposures were estimated by combining the estimates of the job-title and the estimate of the individual random effect corrected for season, filter type, and/or smoking status.
 
PM2.5 (n=5), EC (n=7), OC (n=7), and soot (n=12) exposure in controls was measured in a subset of the controls per factory except for the beer factory where no measurements could be obtained. Levels were averaged (geometric mean) by factory and assigned to all controls in that factory. Minimal variation between factories was observed and the average of all factories was assigned to the beer factory.

Subject enrolment

In March 2013, we conducted a cross-sectional molecular epidemiology study of 54 workers exposed to DEE in this engine- testing facility and 55 controls, which was integrated into a regular health exam administered by the local Center for Disease Control (CDC). These DEE exposed workers, all of whom are male, spend most of their shift in direct proximity to the engines being tested and, as a consequence, have the potential for exposure to substantial levels of DEE. Fifty-five unexposed male workers, frequency-matched to the exposed workers by age within 5 years and smoking status (ie, never, former, current), were identified in control workplaces in the same local region of China with work processes that do not involve exposure to DEE, other types of particulates, or any known or suspected genotoxic, hematotoxic or immunotoxic chemicals. The participation rates for DEE exposed workers and controls were approximately 90% and 80%, respectively. Peripheral blood samples were collected in an EDTA vacutainer tube for the complete blood cell count (CBC) and differential analysis by the Sysmex platform and in a heparin vacutainer for analysis of the major lymphocyte subsets by a FACSCalibur. Coeffcients of variation for all cell counts from the CBC with differential and lymphocyte subsets were ≤5%, with the exception of basophils (13%) and eosinophils (11%). The study was approved by Institutional Review Boards at the US National Cancer Institute and the National Institute of Occupational Health and Poison Control, China CDC. Participation was voluntary and all subjects gave written informed consent.

Statistical analysis

Unadjusted summary measures are presented for all end points. Linear regression using the natural logarithm (ln) of data derived from the CBC, differential and lymphocyte subsets was used to test for differences between workers exposed to DEE and controls, and to conduct trend tests. All statistical models were adjusted for the matching variables, age (as a continuous variable) and smoking status (current, former, never). Potential confounders that have been previously shown to influence one or more of the end points in this report were also included in the fial models, that is, current alcohol consumption (yes/no), recent infection (ﬂu or respiratory infections in the previous month) and body mass index (BMI). All analyses were carried out using SAS V.9.2 software (SAS Institute, Cary, North Carolina, USA).

RESULTS

Demographic characteristics of exposed and unexposed subjects are shown in table 1 and were comparable with regard to age, BMI, current smoking status and recent infection (table 1). Unexposed subjects had a higher proportion of alcohol drinkers than exposed subjects but the difference was not statistically significant. Overall, study subjects were relatively light smokers (mean (SD) 13.2 (6.4) and 12.3 (7.3) cigarettes per day among current smokers in the DEE exposed and control groups, respectively).

There was a wide range of exposure to EC (median, range: 49.7, 6.1–107.7 mg/m3) and other constituents of DEE including organic compounds, soot and PM2.5 (table 2). We present unadjusted exposure values and values that are adjusted for exposure levels in the control factories that were assumed, due to the absence of any DEE or particulate matter exposure sources, to reflect background outdoor levels in this region.

Among the DEE exposed workers, the correlation was very high between EC and OC (Spearman R=0.86, p<0.0001) and soot (R=0.91, p<0.0001). We use EC as the primary exposure proxy for DEE for analyses of biological end points since EC is considered a specific marker for DEE in occupational settings and has been used in the most recent epidemiological investigations of lung cancer.7 8 There was no correlation between EC and PM2.5 among the DEE exposed subjects (R=0.09, p=0.53). The total lymphocyte count and three of four major lymphocyte subsets including CD4+ T cells, CD8+ T cells and B cell counts were statistically significantly increased in workers exposed to DEE compared to controls (table 3), with 16–25% higher cell counts in exposed versus control workers. In contrast, there was no evidence that the fourth major lymphocyte subset, natural killer (NK) cells, was elevated among DEE exposed workers. Also, total T cells were highly significantly elevated in DEE exposed workers compared to controls (p=0.00044). In addition, the basophil count was elevated in DEE exposed workers compared to controls. More extensive adjustment in the analysis for tobacco intensity, duration and pack-years, and for alcohol intensity, did not alter the findings (not shown). Further, we conducted sensitivity analyses by excluding one group of control workers at a time and comparing the remaining controls to DEE exposed subjects; findings were minimally altered (not shown).

To evaluate the consistency of these findings by smoking status, we conducted analyses among current, former and never smokers. Although these subgroups were relatively small, especially never smokers, we found that the above patterns were highly consistent across the three groups (see figure 1 and online supplementary tables S1a–c). However, statistically significant differences were present only for effects among current and former smokers, not among never-smokers.

We observed statistically signifiant exposure–response relationships with lymphocytes and lymphocyte subsets (with the exception of NK cells) with air levels of EC (table 4) comparing controls and DEE exposed workers categorised into tertiles of exposure based on personal air monitoring for EC. Similar results were obtained using DEE as a continuous exposure variable. In analyses conducted among the DEE exposed workers only, there was a significant exposure–response relationship for CD4+ T cells (table 4). Further adjustment in the analysis for duration of employment did not alter the findings (not shown). There was no association between air levels of PM2.5 and other peripheral blood counts among the DEE exposed workers.
