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Extended report
Ambient air pollution exposures and risk of rheumatoid arthritis: results from the Swedish EIRA case–control study
  1. Jaime E Hart1,2,
  2. Henrik Källberg3,
  3. Francine Laden1,2,4,
  4. Tom Bellander3,
  5. Karen H Costenbader5,
  6. Marie Holmqvist3,
  7. Lars Klareskog3,
  8. Lars Alfredsson3,
  9. Elizabeth W Karlson5
  1. 1Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
  2. 2Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
  3. 3Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
  4. 4Exposure, Epidemiology, and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA
  5. 5Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
  1. Correspondence to Dr Jaime E Hart, Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA; Jaime.hart{at}channing.harvard.edu

Abstract

Objective Environmental factors may play a role in the development of rheumatoid arthritis (RA). We examined whether long-term exposures to air pollution were associated with the risk of RA in the Swedish Epidemiological Investigation of Rheumatoid Arthritis Study.

Methods We studied 1497 incident RA cases and 2536 controls. Local levels of particulate matter (PM10) and gaseous pollutants (sulphur dioxide (SO2) and nitrogen dioxide (NO2)) from traffic and home heating were predicted for all residential addresses. We examined the association of an IQR increase (2 µg/m3 for PM10, 8 µg/m3 for SO2 and 9 µg/m3 for NO2) in each pollutant at different time points before symptom onset and average exposure with the risk of all RA and the risk of the rheumatoid factor and anti-citrullinated protein antibody (ACPA) RA phenotypes.

Results There was no evidence of an increased risk of RA with PM10. Total RA risks were modestly elevated for the gaseous pollutants, but were not statistically significant after adjustment for smoking and education (OR 1.18, 95% CI 0.97 to 1.43 and OR 1.09, 95% CI 0.99 to 1.19 for SO2 and NO2 in the 10th year before onset). Stronger elevated risks were observed for individuals with less than a university education and with the ACPA-negative RA phenotype.

Conclusions No consistent overall associations between air pollution in the Stockholm area and the risk of RA were observed. However, there was a suggestion of increased risks of RA incidence with increases in NO2 from local traffic and SO2 from home heating sources with stronger associations for the ACPA-negative phenotype.

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Introduction

Rheumatoid arthritis (RA) is a chronic systemic inflammatory disease affecting approximately 1% of the adult population.1–4 Epidemiological studies have revealed that the risk of developing RA is associated with exposures to silica, mineral oil and cigarette smoke,5–19 suggesting that respiratory exposures activating the immune system may lead to RA, in particular the subset of RA that is characterised by the presence of antibodies to citrullinated protein antigens (ACPA-positive RA).20–22 Air pollution is an environmental factor that has been hypothesised to be associated with an increased risk of RA due to its ability to increase systemic inflammation.23 ,24 In a previous analysis in a cohort of US women, we observed a 30% increased risk of RA in women with residential addresses within 50 m of a major roadway,24 suggesting a possible association with air pollution. In the current analysis we investigate the association of specific air pollutants related to local traffic and home heating sources and the onset of RA in the Swedish Epidemiological Investigation of Rheumatoid Arthritis (EIRA) case–control study to determine if specific air pollutants are associated with an increased risk of RA.

Materials and methods

Study population and outcome assessment

EIRA is a population-based case–control study throughout the middle and southern portions of Sweden. Case–control selection was described in detail previously.14 Briefly, each case was defined as a person 18–70 years of age who, between 1996 and 2008, received a diagnosis of RA for the first time. All potential cases were examined and diagnosed by a rheumatologist at the rheumatology unit entering the case into the study, and all fulfilled the American College of Rheumatology 1987 criteria for RA. The time of onset of RA was defined as the time of the first symptom of the disease. For 85% of the cases the time between onset and inclusion in the study was less than 1 year. Each case was further classified by autoantibody phenotypes according to the presence of rheumatoid factor (RF) or anti-citrullinated peptide antibodies (ACPA). Controls were identified from the national population register and were selected with consideration for the age and gender of the cases. All cases and controls consented to the study after receiving written information, and all aspects of the study were approved by the ethics committee of Karolinska Institutet. For this analysis, only cases and controls residing in Stockholm County (where the pollution estimates were available), a geographical region of 6488 km2, at the time of enrolment were included resulting in a total of 1497 cases and 2536 controls.

Exposure assessment

All addresses occupied for at least 2 years in the residential history of each EIRA participant from 1968 onwards were geocoded through Statistics Sweden to obtain latitude and longitude. Prediction models were used to estimate annual air pollution levels from local sources of traffic (particulate matter ≤10 microns in aerodynamic diameter (PM10), particulate matter ≤2.5 microns in aerodynamic diameter (PM2.5), nitrogen dioxide (NO2) and oxides of nitrogen) and home heating (sulphur dioxide, SO2) for each home address. These estimates were available for all addresses in Stockholm County 1968–1998 based on models developed and described in detail for previous studies.25–27 Briefly, these models combined information from the emissions inventories (available each decade since 1960), annual changes in road traffic and annual changes in residential heating and fuel sulphur content with dispersion modelling to determine the levels of pollution at each address in each year. We assumed that the levels of pollution were constant after the last available exposure predictions. Due to the modelling process and similar patterns in dispersion, there is a high level of correlation between PM10 and PM2.5 and between NO2 and oxides of nitrogen therefore we have chosen only to present models using the predictions for PM10 and NO2.

We created a variety of exposure metrics as appropriate to examine different potential important periods of exposure. To explore the effects of timing of exposure before disease incidence, we created metrics to examine the annual exposure in the 5th, 10th and 20th years before the onset of RA symptoms or the index date for the corresponding controls, where the index date for a control is the date of onset of RA for the case that the control was originally matched to. These windows of exposure were chosen based on previous studies demonstrating that autoantibodies are elevated 5–10 years before diagnosis with RA.28–30 As it is also plausible that long-term exposure to air pollution is the exposure of interest, we calculated the average exposure from either birth or the start of air pollution predictions (whichever was later) to diagnosis for all addresses in Stockholm County.

Additional covariates

Information on potential confounders is available from mailed questionnaires that were completed by incident RA cases and controls. Incomplete questionnaires were completed with the assistance of trained staff over the phone or by mail. We adjusted for age and gender to account for the matching in the initial design of the study and examined possible confounding by smoking status (current/former/never), and level of educational attainment as a marker of individual socioeconomic status (SES). As used previously in this study,31 education was based on categories used by Statistics Sweden: compulsory school, vocational upper secondary school, theoretical upper secondary school, other education and university degree.

Statistical analysis

As matching was broken for this analysis because of the availability of cases and controls with air pollution data, logistic regression models were used to assess the relationship of total RA, RF-positive and RF-negative RA and ACPA-positive and ACPA-negative RA with exposures to each of the pollutants in separate models. All models were controlled for the original matching factors—age and gender. To allow comparability between pollutants, OR and 95% CI were calculated based on an IQR increase (IQR, difference between the 75th and 25th percentiles in the distribution) of the average exposure for each pollutant (2 µg/m3 for PM10, 8 µg/m3 for SO2 and 9 µg/m3 for NO2). To test for deviations from linearity for each dose–response we used cubic splines and only present linear results if there were no statistically significant departures from linearity. To determine if educational attainment was an effect modifier, we performed stratified analyses in cases and controls with and without a university degree to obtain category-specific OR. In previous analyses in EIRA, smoking has been shown to be an important effect modifier, therefore we also performed analyses stratified by ever/never smoking status. To test for statistical significance (p<0.05) of the effect modifiers we included multiplicative interaction terms in the multivariable analyses of the full study. All statistical analyses were performed in SAS V.9.1.3.32

Results

The EIRA cases/controls both had a mean age of 51.5 years (SD 12.6) at enrolment (table 1). Seventy-three per cent of the cases and 71% of the controls were women, and 28.1% and 34.9%, respectively, were never smokers. Twenty-two per cent of the cases and 16% of the controls had a compulsory school education, while 24% of the cases and 30% of the controls had a university education. Overall, the median levels of air pollution were similar between cases and controls. For PM10 there was little difference in the median levels across the different time windows examined; however, for NO2 and SO2, the levels tended to be higher in cases than in controls, and exposures were higher in windows that included exposures further back in time.

Table 1

Characteristics of the EIRA cases and controls

The results for the EIRA models including all RA cases are presented in table 2. Overall, there was no evidence of an association of RA with exposures to PM10 in any time period and the OR were mostly below 1 with wide CI. In models adjusted for age and gender (model 1), exposure to SO2 and NO2 did appear to be associated with a modest increased risk of RA. The elevated risks with exposure to the gaseous pollutants were mainly observed with exposure in the 10th and 20th years before onset. Models additionally adjusted for smoking status were generally attenuated (model 2) and the point estimates for the gaseous pollutants were generally elevated but not statistically significant after additional adjustment for education level (model 3). We did not see an association with RA risk for any of the pollutants with the average exposure metric.

Table 2

OR and 95% CI of incident RA risk associated with an IQR increase in ambient air pollution

Results for the ACPA-positive and ACPA-negative RA phenotypes are presented in table 3 and the results for RF-positive and RF-negative RA are presented in the supplementary appendix (available online only). Overall, the patterns in the specific antibody subtypes were similar to those seen for all RA cases. However, the effects of SO2 and NO2 did appear to be strongest in models restricted to the ACPA-negative cases, with elevations, but few statistically significant associations, observed for ACPA-positive, RF-positive or RF-negative RA. The age, gender and smoking adjusted OR for each IQR increase in SO2 in the 10th year before symptom onset was 1.44 (95% CI 1.10 to 1.90) for ACPA-negative RA and for NO2 was 1.18 (95% CI 1.03 to 1.34). Further adjustment for education modestly strengthened the associations. OR were slightly lower for the gaseous pollutants in the 5th and 20th year before symptom onset and again were not elevated for the measure of average exposure.

Table 3

OR and 95% CI of ACPA-positive and ACPA-negative RA risk associated with an IQR increase in ambient air pollution in EIRA

In stratified models (figure 1), OR for all RA, ACPA-positive RA and ACPA-negative RA were elevated in participants without a university education compared to those with a university education, although elevated OR were observed in both groups for the gaseous pollutants. Most interaction terms for all RA and ACPA positive RA were significant at the p<0.05 level with the exception of three SO2 interactions (10th year before onset: p for interaction 0.69 for all RA, p for interaction 0.56 for ACPA-positive RA; 20th year before onset p for interaction 0.09 for all RA) and the interaction of PM10 in the 20th year before onset for ACPA-positive RA (p for interaction 0.16). No interaction terms were statistically significant for ACPA-negative RA, possibly due to the smaller strata-specific sample sizes. In models stratified by smoking (figure 2), total RA OR were generally higher for never smokers compared to ever smokers; however, none of the smoking pollution interaction terms were statistically significant.

Figure 1

The effect of educational attainment on incident rheumatoid arthritis (RA) risk (A), anti-citrullinated protein antibody (ACPA)-positive RA risk (B), and ACPA-negative RA risk (C) associated with an IQR increase in ambient air pollution in the Epidemiological Investigation of Rheumatoid Arthritis study (OR and 95% CI). All models are adjusted for age, gender and smoking. NO2, nitrogen dioxide; PM10, particulate matter; SO2, sulphur dioxide.

Figure 2

The effect of smoking status on Incident rheumatoid arthritis (RA) risk (A), anti-citrullinated protein antibody (ACPA)-positive RA risk (B), and ACPA-negative RA risk (C) associated with an IQR increase in ambient air pollution in the Epidemiological Investigation of Rheumatoid Arthritis study (OR and 95% CI). All models are adjusted for age, gender and educational attainment. NO2, nitrogen dioxide; PM10, particulate matter; SO2, sulphur dioxide.

Discussion

In the EIRA case–control study, exposures to gaseous pollutants from local sources from traffic (NO2) and home heating (SO2) in the 5th, 10th and 20th year before RA symptom onset were positively, but in most cases not statistically significantly, associated with overall RA risk. Significant but still weak associations were observed between exposure to certain gaseous pollutants and all RA in certain strata of the population in particular. When subdivided by serology (RF and ACPA), the adverse effects of NO2 and SO2 were stronger and more often statistically significant for ACPA-negative RA cases compared to the results in all RA cases combined, while similar trends but no statistically significant associations were observed in the ACPA-positive, RF-positive or RF-negative analyses. These findings are in contrast to associations demonstrated for other airway environmental exposures, cigarette smoking and silica dust, in which the strongest associations are seen for ACPA-positive RA.20–22 ,33 We found little evidence of adverse effects of increases in particulate matter, PM10, on the risk of RA or with average exposure measurements. To the best of our knowledge, this is the first study to examine the association of exposure to specific air pollutants and the risk of RA.

RA is an inflammatory autoimmune disease and various air pollutants have been linked with other diseases of pulmonary and systemic inflammation.23 The current study does not provide strong evidence for an association of air pollution with the risk of RA. This is in contrast to our previous study in US women, showing a 30% elevated risk of RA with residence within 50 m of a major roadway.24 In the current study, NO2 and PM10 were explicitly modelled based only on local traffic sources; however, we only saw an increased risk of RA with NO2. NO2 is often considered an ideal marker of traffic pollution, and increased exposure has been linked with a variety of adverse health effects and increases in systemic inflammation, particularly in European studies.34–39 SO2 was modelled to reflect the impact of local home heating sources. Long-term exposures to SO2 have not been consistently associated with chronic diseases or all-cause mortality,40–45 but more acute exposures to SO2 have been related to cardiovascular deaths and/or hospital admissions.46 It is possible that in this study SO2 is a proxy for other exposures that may be aetiologically relevant for RA. Our results suggest that exposures in the specific years before symptom onset may be more strongly associated with the development of RA than with the long-term average exposures. Recent studies have suggested that autoantibodies such as ACPA have been found to precede the onset of RA by up to 14 years,28–30 and an aetiology for RA has been proposed that builds on the exposure in the lungs to irritants/adjuvants such as smoking and silica.22 ,47 Our initial hypothesis was that if this was the primary biological mechanism affected by air pollution (another lung irritant), the strongest effects would be observed in ACPA-positive cases. This mechanism would not explain our stronger findings in the ACPA-negative cases, which may indicate the role of other aetiological mechanisms.

This analysis has several important limitations as well as strengths. Although exposure data in EIRA are available over a 30-year period (1968–98) allowing us to examine the impacts of exposure at various time points before RA onset, this study is restricted to a small geographical area, Stockholm County. We are only examining the contribution of local sources of pollution, in an area with comparatively low background levels; therefore, the range of each pollutant is quite tight, making it difficult to observe effects. Our measures of air pollution are indicators of complex mixtures, and since our estimates of NO2 and PM10 are both describing air pollution from traffic, they are highly correlated (correlations ∼0.95 within each period of exposure). However, the range of PM10 was much smaller (2 µg/m3) than that of NO2 (9 µg/m3), possibly explaining the differences in the findings between the two pollutants. The exposure models used are imperfect predictors of personal exposures and we do not have information on the amount of time that each participant spent at the actual home address, or time spent inside or outside the home. This would most likely lead to non-differential misclassification of our air pollution exposure measures, and would bias our results towards finding no association. Finally, we lack information on exposure to these pollutants at locations other than the residence and we do not have information on occupational PM10, SO2, or NO2 exposures. This prevents us from examining the impact of exposures from all sources on the incidence of RA.

SES was an important confounder and a significant modifier in our EIRA analyses. In the air pollution literature, it has been hypothesised that individuals with lower SES may be particularly susceptible to the effects of air pollution, or that they may experience higher indoor levels of ambient air pollution due to the conditions of poor housing stock.48 Low SES could also be a marker for residential exposure to air pollution, as individuals with higher SES tend to live in areas with lower levels of pollution.48 In the EIRA participants, levels of pollution were modestly higher for individuals with less than a university education, compared to those with a university education, and results from stratified analyses indicated that the effect of air pollution is most pronounced for those with less than a university education. In contrast, although smoking status was an important confounder in our analyses, there was little evidence of effect modification. However, results tended to be slightly stronger among never smokers, who may be more susceptible to the effects of air pollution than smokers consistently exposed to cigarette smoke.

In conclusion, we do not find any consistent overall associations between air pollution in the Stockholm area and the risk of RA. There was a suggestion of increased risks of RA incidence with increases in NO2 from local traffic and SO2 from home heating sources with stronger associations for the ACPA-negative phenotype. SES appeared to be an important confounder and effect modifier, suggesting that further study is needed in populations with a wide range of SES. As this is, to the best of our knowledge, the first study to examine this association, further research into whether particulate matter or gaseous pollutants are associated with RA risk is needed, particularly in locations with a wider range of pollution exposures.

References

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Supplementary materials

  • Supplementary Data

    This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Footnotes

  • LA and EWK contributed equally to the manuscript.

  • Contributors JEH assisted with study design, performed the data analysis, and wrote and revised the manuscript. FL, KHC, LK, LA and EWK conceived and designed the study, assisted with interpretation of results, revised the manuscript, and reviewed the final manuscript. EWK and LA contributed equally to the manuscript. TB provided exposure data, assisted with interpretation of results, revised the manuscript, and reviewed the final manuscript. HK and MH assisted with data collection and cleaning, revised the manuscript, and reviewed the final manuscript.

  • Funding This study was supported by NIH grants AR047782 and AR052403, the Swedish Medical Research Council, Swedish Council for Working Life and Social Research, Swedish Medical Research Council, the insurance company AFA, FAMRI (Flight Attendant Medical Research Institute), and the COMBINE (Controlling Chronic Inflammatory Diseases with Combined Efforts) project.

  • Competing interests None.

  • Patient consent Obtained.

  • Ethics approval All aspects of the study were approved by the ethics committee of Karolinska Institutet.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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