Abstract
Objective
Environmental factors may play a role in the development of rheumatoid arthritis (RA), and we have previously observed increased RA risk among women living closer to major roads (a source of air pollution). We examined whether long-term exposures to specific air pollutants were associated with RA risk among women in the Nurses’ Health Study.
Methods
The Nurses’ Health Study (NHS) is a large cohort of U.S. female nurses followed prospectively every two years since 1976. We studied 111,425 NHS participants with information on air pollution exposures as well as data concerning other lifestyle and behavioral exposures and disease outcomes. Outdoor levels of different size fractions of particulate matter (PM10 and PM2.5) and gaseous pollutants (SO2 and NO2) were predicted for all available residential addresses using monitoring data from the USEPA. We examined the association of time-varying exposures, 6 and 10 years before each questionnaire cycle, and cumulative average exposure with the risks of RA, seronegative (rheumatoid factor [RF] and anti–citrullinated peptide antibodies [ACPA]) RA, and seropositive RA.
Results
Over the 3,019,424 years of follow-up, 858 incident RA cases were validated by medical record review by two board-certified rheumatologists. Overall, we found no evidence of increased risks of RA, seronegative or seropositive RA, with exposure to the different pollutants, and little evidence of effect modification by socioeconomic status or smoking status, geographic region, or calendar period.
Conclusion
In this group of socioeconomically-advantaged middle-aged and elderly women, adult exposures to air pollution were not associated with an increased RA risk.
Rheumatoid arthritis (RA) is a chronic systemic inflammatory disease affecting approximately 1% of the adult population (1–4). Epidemiologic studies have revealed that risk of developing RA is associated with exposures to cigarette smoke, silica and mineral oil (5–20), suggesting that respiratory exposures activating the immune system may lead to RA.
In the Nurses’ Health Study (NHS), a large prospective cohort of US female registered nurses, we previously examined the association between incidence of RA and distance to the nearest major road, as a marker of traffic exposure (21). We observed a 30% increased risk of RA in women living within 50 meters of a major road, compared to those women living at least 200 meters away, suggesting a possible association with air pollution. In a recent analysis in the Swedish Epidemiological Investigation of Rheumatoid Arthritis (EIRA) case-control study, we examined the association of exposure to several specific air pollutants, including SO2, NO2, and particulate matter less than 10 microns in aerodynamic diameter (PM10) from local sources (traffic and home heating) within Stockholm County with RA risk (22). While, there were no consistent overall associations between air pollution and risk for RA, we did observe a suggestion that selected pollutants (NO2 and SO2) were associated with increased risk of RA. Our objective in the present study was to conduct similar analyses examining associations of specific air pollutants with risk of RA within the NHS.
Materials and Methods
Study Population
The Nurses’ Health Study (NHS) is a long-term prospective cohort study of US nurses. NHS was initiated in 1976 when 121,700 US registered female nurses, 30 to 55 years old, completed a mailed questionnaire. At the study inception, women resided in eleven states, scattered across the US (California, Connecticut, Florida, Massachusetts, Maryland, Michigan, New Jersey, New York, Ohio, Pennsylvania, and Texas). However, by 1986, members of the cohort had moved to live throughout the US, with at least 10 nurses in every state. Follow-up questionnaires, with response rates above 90%, are mailed every two years to update information on risk factors and the occurrence of major illnesses. This also provides a detailed residential history for each participant which is available electronically starting in 1986. Women were included in the current study if they had at least one home address within the continental United States 1986–2006 and had no history of RA or other connective tissue disease at baseline in 1976. A total of 111,425 participants were available for analysis.
Outcome Assessment
RA was confirmed among all nurses reporting doctor-diagnosed RA by a connective tissue symptom screening questionnaire (23) followed by medical record review as in prior publications(24). Subjects who self-reported but later denied RA diagnosis, denied permission to obtain medical records, or had a negative screening questionnaire were excluded. For this analysis, we identified total of 858 confirmed incident RA cases (1976–2006). Information on the presence of rheumatoid factor [RF] or anti–citrullinated peptide antibodies [ACPA] was extracted from the medical records and used to classify RA phenotypes as seropositive (RF and/or ACPA positive) or seronegative RA (RF and ACPA negative). The protocol was approved by the human subjects committee at Brigham and Women’s Hospital and participants provided implied informed consent to participate.
Exposure Assessment
Residential address information is updated in the NHS cohort every two years as part of the questionnaire mailing process, and has been geocoded to obtain latitude and longitude for each address from 1986–2006. Predicted monthly information on ambient PM10 and PM2.5 (1988–2007) was available for each address from spatiotemporal models incorporating a combination of spatial smoothing of US Environmental Protection Agency monitoring values, meteorology, and GIS-based covariates such as elevation, population density, distance to roadways, and point-source emissions (25, 26). Annual average exposures to SO2 and NO2 (1985–2000) were available from different prediction models using a similar approach of spatial smoothing of monitoring information and GIS-based covariates including elevation, population density, distance to roadways, and distance and emissions of the nearest power plant (27). We assumed that the levels of pollution were constant from 1976 (cohort start) to 1986 (first available air pollution data), then used updated exposure estimates every two years based on geocoded address data. From 1976–1986, 40.8% of the nurses moved to a different address. In sensitivity analyses, we excluded RA cases diagnosed before 1986, including only cases diagnosed after exposure data was available.
We created three exposure metrics to examine different potential important windows of exposure. To explore the effects of timing of exposure prior to disease incidence, we created metrics to examine the time-varying annual exposure the 6th- and 10th-year prior to each questionnaire cycle. These windows of exposure were originally chosen in the EIRA study based on previous studies demonstrating that antibodies are elevated 5–10 years prior to diagnosis with RA (28–30). As it is also plausible that longer-term exposure to air pollution is the exposure of interest, we calculated the time-varying cumulative average exposure during the follow-up period.
Additional Covariates
Information on potential confounders is available in NHS from the biennial questionnaires. Therefore, when appropriate, each woman was assigned updated covariate values every two years. We examined possible confounding by age (in months), race, age at menarche, parity, total months of lactation, current menopausal status, menopausal hormone use, oral contraceptive use, physical activity, and body mass index, many of which have been shown to be risk factors for RA (24, 31). To control for smoking, we used data from the lifetime smoking history to calculate pack-years (number of packs/day multiplied by number of years of cigarette smoking) and current smoking status (current/former/never) (20). To control for individual level socioeconomic status, we considered several variables, including nurse’s educational level, occupation of both parents, marital status, and if applicable, husband’s education. To control for area-level socioeconomic status, we included area level information on Census tract level median income and house value.
Statistical Analysis
Time-varying Cox proportional hazards models were used to assess the relationship of incident RA (1976–2006) with each of the air pollutants in separate models. Person-time accrued from January 1, 1976 until diagnosis of RA, loss to follow-up, date of death, or the end of follow-up, whichever was first. Person-time (3% of the total) was excluded from follow-up for any period in which the home address was outside of the continental U.S. or pollution predictions were not available. Hazard ratios (HRs) and 95% CIs were calculated based on an interquartile range change (difference between the 75th and 25th percentiles of the distribution) in each pollutant after determining that the dose-response was not statistically significantly different from linear using splines. All Cox models were stratified by age in months and calendar year. Previous analyses in EIRA suggested possible effect modification by socioeconomic status (SES), with higher risks observed in those with lower SES (22). To examine potential effect modification by SES in the NHS, we performed stratified analyses by level of husband’s education (the best available indicator of individual SES in this cohort) to obtain category specific hazard ratios and created multiplicative interaction terms to test for statistical significance (p<0.05). Smoking status has also been an important effect modifier of RA in the NHS (32). Therefore, we similarly examined effect modification by ever/never smoking status. Lastly, to determine if there were important regional or temporal differences in the risk of RA with pollution, we examined effect modification by US Census region (Northeast, South, Midwest, West) and calendar period (1976–1986, 1987–1996, 1997–2006). In sensitivity analyses, we also limited the study follow-up to the periods in which air pollution predictions were directly available to determine if our assumptions of static levels were important. All statistical analyses were performed in SAS version 9.1.3 (Cary, NC) (33).
Results
Throughout the entire period of follow-up (1976–2006), NHS participants were on average 55.9 (SD= 10.9) years of age. Forty-three percent were never smokers and most (73%) had a registered nursing degree (Table 1). As expected in this nationwide study, there was a wide range of exposure for all of the pollutants examined, with wider distributions for NO2 and SO2 than for particulate matter (Table 2). The distributions of each pollutant were similar for the various exposure metrics examined.
Table 1.
Selected Age-Standardized Characteristics of the NHS Participants (N=111,425) Over the Full Period of Follow-Up 1976–2006
| Age (in years), meana | 55.9 (10.9) |
| Pack-years of smoking, meanb | 12.8 (18.6) |
| Caucasian race, % | 94 |
| Smoking status, % | |
| Current | 20 |
| Former | 35 |
| Never | 43 |
| Parity/Lactation, % | |
| Nulliparous | 7 |
| Parous, never breastfed | 30 |
| Parous, breastfed 1–11 months | 36 |
| Parous, breastfed 12+ months | 16 |
| Menopausal Status, % | |
| Premenopausal | 25 |
| Postmenopausal | 71 |
| Unknown Status | 3 |
| Postmenopausal Hormone Use, %c | |
| Never Used | 24 |
| Past Use | 17 |
| Current Use | 21 |
| Oral Contraceptive Use, % | |
| Never Used | 51 |
| Ever Use | 45 |
| Physical activity (MET-hours/week), % | |
| <3 | 19 |
| 3–<9 | 19 |
| 9–<18 | 15 |
| 18–<27 | 9 |
| ≥27 | 14 |
| Father’s occupation, % | |
| Professional/Manager | 25 |
| Other Job | 75 |
| Mother’s occupation, % | |
| Housewife | 64 |
| Other Job | 36 |
| Education | |
| RN | 73 |
| Other | 27 |
| Marital Status | |
| Married | 64 |
| Other | 36 |
| Husband’s Education | |
| Missing or Not Applicable | 34 |
| Less than high school | 4 |
| High school | 26 |
| Greater than high school | 35 |
| Median census tract family income, mean | 59,602 (28,252) |
| Median census tract household value, mean | 159,880(128,329) |
Value is not age-standardized
Percentages may not sum to 100% due to missing values
Table 2.
Predicted Ambient Air Pollution Levels During Follow-Up
| Exposure Metric | NO2 (μg/m3) | SO2 (μg/m3) | PM10 (μg/m3) | PM2.5 (μg/m3) |
|---|---|---|---|---|
| 6th Yr Prior | ||||
| Mean (SD) | 33.9 (14.2) | 20.4 (10.8) | 28.3 (7.1) | 16.8 (3.7) |
| Median (IQR) | 32.6 (15.3) | 21.8 (14.4) | 27.2 (7.6) | 16.9 (5.1) |
| Min, Max | 0, 171.4 | 0, 128.6 | 5.1, 83.8 | 2.5, 32.7 |
| 10th Yr Prior | ||||
| Mean (SD) | 34.3 (14.1) | 20.9 (10.7) | 28.7 (7.0) | 17.0 (3.7) |
| Median (IQR) | 32.9 (15.0) | 22.3 (14.1) | 27.6 (7.5) | 17.2 (4.9) |
| Min, Max | 0, 171.4 | 0, 123.4 | 5.1, 81.9 | 2.5, 31.3 |
| Cumulative Average | ||||
| Mean (SD) | 33.3 (13.9) | 19.7 (10.4) | 27.7 (6.8) | 16.5 (3.6) |
| Median (IQR) | 31.9 (14.9) | 20.7 (14.1) | 26.6 (7.3) | 16.6 (4.9) |
| Min, Max | 0, 171.4 | 0, 124.1 | 5.1, 82.1 | 2.5, 31.4 |
During 3,019,423.5 person-years of follow-up there were a total of 858 incident cases of RA (58.4% seropositive) among the 111,425 women. Overall, there was no evidence of an increased risk of total RA with exposures to air pollution (Table 3). On the contrary, the HRs were mostly below 1, and in some cases statistically significant inverse effects were observed. Patterns were similar in models restricted to the seropositive or seronegative RA phenotypes (Table 4). Results from models stratified by husband’s education as a measure of individual-level SES are presented in Figure 1. Although not statistically significantly different, overall the group of women who were not married, or whose husbands had a high school education or less tended to have slightly higher hazard ratios compared to the group of women whose husbands had greater than a high school education. Results stratified by smoking status are presented in Figure 2. With the exception of SO2, in general, never smokers tended to have lower HRs associated with air pollution exposures compared to ever smokers, and only the interaction terms for NO2 and smoking were statistically significant (p for interaction =0.02 for the 6th year prior and p for interaction=0.01 for the 10th year prior). No statistically significant differences were observed by Census region of residence or calendar period. Additionally, our findings were similar in models restricted to periods in which predictions of air pollution were directly available (1986-).
Table 3.
Hazard Ratios and 95% Confidence Intervals of Incident RA Risk Associated with an Interquartile Range Increase in Ambient Air Pollution in the NHS cohort (n=111,425, 858 cases)
| Timing of Time-Varying Pollution | NO2 (15 μg/m3) Hazard Ratio (95%CI) |
SO2 (14 μg/m3) Hazard Ratio (95%CI) |
PM10 (7 μg/m3) Hazard Ratio (95%CI) |
PM2.5 (5 μg/m3) Hazard Ratio (95%CI) |
|---|---|---|---|---|
| 6th Year Prior | ||||
| Model 1a | 0.92 (0.85–0.99) | 1.00 (0.92–1.09) | 0.91 (0.85–0.98) | 0.94 (0.86–1.03) |
| Model 2b | 0.92 (0.85–0.99) | 1.01 (0.92–1.10) | 0.92 (0.86–0.99) | 0.95 (0.87–1.05) |
| Model 3c | 0.94 (0.87–1.01) | 1.00 (0.91–1.10) | 00.93 (0.86–1.00) | 0.95 (0.87–1.04) |
| 10th Year Prior | ||||
| Model 1a | 0.91 (0.85–0.98) | 0.99 (0.91–1.08) | 0.91 (0.85–0.98) | 0.93 (0.85–1.02) |
| Model 2b | 0.91 (0.84–0.98) | 1.00 (0.91–1.09) | 0.92 (0.87–1.05) | 0.95 (0.86–1.04) |
| Model 3c | 0.92 (0.85–1.00) | 0.99 (0.90–1.08) | 0.93 (0.86–0.99) | 0.95 (0.85–1.03) |
| Cumulative Average | ||||
| Model 1a | 0.91 (0.84–0.98) | 0.99 (0.91–1.09) | 0.90 (0.84–0.97) | 0.93 (0.85–1.02) |
| Model 2b | 0.91 (0.84–0.98) | 1.00 (0.91–1.10) | 0.91 (0.85–0.98) | 0.95 (0.86–1.04) |
| Model 3c | 0.92 (0.85–1.00) | 0.99 (0.90–1.09) | 0.92 (0.85–0.99) | 0.94 (0.86–1.04) |
Adjusted for current age in months, race, and calendar year
Adjusted for current age in months, race, smoking status (current, former, never), education level
Adjusted for current age in months, race, smoking status (current, former, never), pack-years of cigarette smoking, age at menarche, parity, duration of lactation, menopausal status, menopausal hormone use, oral contraceptive use, race, physical activity, body mass index, mother and father’s occupation, education level, martial status, husband’s education, and Census tract level median family income and house value
Table 4.
Hazard Ratios and 95% Confidence Intervals of Incident Seropositive or Seronegative RA Risk by Associated with Interquartile Range Increase in Ambient Air Pollution in the NHS cohort (n=111,425)
| Timing of Time-Varying Pollution | Seronegative RA (357 cases) | Seropositive RA (501 cases) | ||||||
|---|---|---|---|---|---|---|---|---|
|
| ||||||||
| NO2 (15 μg/m3) Hazard Ratio (95%CI) |
SO2 (14 μg/m3) Hazard Ratio (95%CI) |
PM10 (7 μg/m3) Hazard Ratio (95%CI) |
PM2.5 (5 μg/m3) Hazard Ratio (95%CI) |
NO2 (15 μg/m3) Hazard Ratio (95%CI) |
SO2 (14 μg/m3) Hazard Ratio (95%CI) |
PM10 (7 μg/m3) Hazard Ratio (95%CI) |
PM2.5 (5 μg/m3) Hazard Ratio (95%CI) |
|
| 6th Year Prior | ||||||||
| Model 1a | 0.93 (0.83–1.04) | 0.98 (0.86–1.12) | 0.99 (0.89–1.10) | 0.99 (0.86–1.14) | 0.90 (0.82–0.99) | 1.02 (0.91–1.14) | 0.85 (0.77–0.94) | 0.90 (0.80–1.02) |
| Model 2b | 0.93 (0.83–1.04) | 0.99 (0.86–1.13) | 1.00 (0.91–1.11) | 1.01 (0.88–1.16) | 0.90 (0.82–1.00) | 1.03 (0.92–1.15) | 0.86 (0.78–0.95) | 0.92 (0.81–1.03) |
| Model 3c | 0.97 (0.86–1.10) | 0.96 (0.83–1.11) | 1.02 (0.92–1.14) | 1.01 (0.87–1.16) | 0.92 (0.83–1.01) | 1.03 (0.91–1.16) | 0.87 (0.79–0.96) | 0.93 (0.82–1.05) |
| 10th Year Prior | ||||||||
| Model 1a | 0.93 (0.83–1.04) | 0.97 (0.85–1.11) | 0.99 (0.89–1.09) | 0.98 (0.86–1.13) | 0.89 (0.81–0.98) | 1.02 (0.91–1.14) | 0.85 (0.77–0.94) | 0.89 (0.80–1.00) |
| Model 2b | 0.93 (0.83–1.04) | 0.97 (0.85–1.12) | 1.00 (0.91–1.11) | 1.00 (0.87–1.15) | 0.89 (0.81–0.99) | 1.03 (0.91–1.15) | 0.86 (0.78–0.95) | 0.91 (0.81–1.02) |
| Model 3c | 0.97 (0.86–1.09) | 0.95 (0.82–1.09) | 1.02 (0.92–1.13) | 0.99 (0.86–1.14) | 0.91 (0.82–1.00) | 1.03 (0.91–1.16) | 0.86 (0.78–0.95) | 0.92 (0.81–1.03) |
| Cumulative Average | ||||||||
| Model 1a | 0.92 (0.82–1.04) | 0.97 (0.84–1.11) | 0.98 (0.88–1.10) | 0.99 (0.86–1.15) | 1.02 (0.90–1.14) | 0.89 (0.80–0.98) | 0.84 (0.76–0.93) | 0.89 (0.78–1.00) |
| Model 2b | 0.93 (0.83–1.04) | 1.03 (0.68–1.55) | 1.00 (0.91–1.11) | 0.99 (0.94–1.04) | 1.03 (0.91–1.16) | 0.89 (0.80–0.98) | 0.85 (0.77–0.94) | 0.90 (0.80–1.02) |
| Model 3c | 0.97 (0.86–1.10) | 0.95 (0.82–1.10) | 1.02 (0.91–1.14) | 1.01 (0.87–1.17) | 1.03 (0.91–1.16) | 0.90 (0.81–1.00) | 0.85 (0.77–0.95) | 0.91 (0.80–1.03) |
Adjusted for current age in months, race, and calendar year
Adjusted for current age in months, race, smoking status (current, former, never), education level
Adjusted for current age in months, race, smoking status (current, former, never), pack-years of cigarette smoking, age at menarche, parity, duration of lactation, menopausal status, menopausal hormone use, oral contraceptive use, race, physical activity, body mass index, mother and father’s occupation, education level, martial status, husband’s education, and Census tract level median family income and house value
Figure 1. The Effect of Husband’s Educational Attainment on Incident RA Risk Associated with an Interquartile Range Increase in Ambient Air Pollution (Hazard Ratios and 95% Confidence Intervals).
All models are adjusted for current age in months, race, smoking status (current, former, never), pack-years of cigarette smoking, age at menarche, parity, duration of lactation, menopausal status, menopausal hormone use, oral contraceptive use, race, physical activity, body mass index, mother and father’s occupation, education level, martial status, and Census tract level median family income and house value.
There were no statistically significant interaction terms.
Figure 2. The Effect of Smoking Status on Incident RA Risk Associated with an Interquartile Range Increase in Ambient Air Pollution (Hazard Ratios and 95% Confidence Intervals).
Asterisks indicate statistically significant (p<0.05) interaction terms.
All models are adjusted for current age in months, race, pack-years of cigarette smoking, age at menarche, parity, duration of lactation, menopausal status, menopausal hormone use, oral contraceptive use, race, physical activity, body mass index, mother and father’s occupation, education level, martial status, husband’s education, and Census tract level median family income and house value
Discussion
In this cohort of middle-aged and elderly women, we found little evidence of adverse effects of air pollution assessed as total ambient exposures on incidence of RA. These are similar to our recent findings in the Swedish EIRA case-control study, where no consistent associations were observed with local-source specific levels of the same pollutants. However, unlike in the EIRA analyses, we did not see increased risks in analyses of any RA phenotypes with exposures to NO2 and SO2.
In our previous study in the NHS, we observed a 30% elevated risk of RA with residence within 50 m of a major roadway, suggesting a possible etiologic role of air pollution (21). However, in the same population, we do not see an elevated risk of RA with predicted measures of ambient pollution. This may be partially explained by the weak association between the individual pollutants and the specific distance to road measurement used in our previous work. Restricting to women with a street level geocoded address in 2000 (the same population as our previous analysis), the correlations between distance to road and the individual pollutants were in the range of 0.05 to 0.11 and there was little difference in the distribution of levels by the distance to road categories. Therefore it is possible that distance to roadway is proxy for some other exposures, such a noise, neighborhood, etc, which may have confounded the association with RA in our previous analyses, or alternatively, that the currently used measurements of air pollution exposures are too imprecise or not strongly associated with the etiologically relevant agent.
This analysis has several limitations. SES was an important confounder and a significant effect modifier in our previous EIRA analyses (22), with higher risk observed among individuals with lower educational attainment. It is possible that the lack of association within the NHS is due to the fact that all participants have high levels of educational attainment and SES, as they were employed as nurses at the start of the study. This also likely reduces the generalizability of these findings to women of similar SES status in the US. Exposure data were not available for the full period of follow-up, meaning that for cases diagnosed 1976–1985, the air pollution exposure data was from after RA onset, with the assumption that levels were ranked the same in earlier time periods. Our assumption of static levels before the available air pollution predictions likely introduced non-differential misclassification, which may partially explain our null findings. However, in sensitivity analyses limited to incident RA after air pollution exposure, our results were similar. Our pollution predictions are intended to represent the total levels of each pollutant experienced at each address, and had a similar distribution to levels measured in the general continental US. In our previous analysis in the EIRA cohort, we assessed pollution levels only from local sources of traffic and home heating. Our current modeling approach includes all sources of these pollutants (local and regional), all of which may not contribute to disease risk. If only local sources of traffic and home heating are associated with RA risk than our inclusion of additional sources of pollution would lead to increased measurement error, limiting our availability to detect associations. Regardless, our measures of ambient air pollution are based on exposure models which are imperfect predictors of personal exposures and we do not have information concerning the amount of time that each participant spent at each home address. Lastly, we lack information on exposure to these pollutants at locations other than the residence. This prevents us from examining the impact of each nurses’ total air pollution exposure experience on the incidence of RA.
As cigarette smoking, crystalline silica and organic solvent exposures, all via respiratory routes, are among the strongest environmental exposures related to increased risk of RA, our hypothesis that ambient air pollution exposure may be related to RA risk as well was well founded. The NHS is the largest population-based cohort followed for many years for exposures prior to the development of RA. However, in this cohort, we were unable to detect any associations between residential air pollution by several metrics and risk of RA. As we have discussed, it is unclear if this lack of association is due to limitations in the ability to accurately assess air pollution exposure, or whether air pollution does not represent a source of increased RA risk among middle-aged to older, mainly Caucasian women living in the US.
Significance and Innovations.
Respiratory exposures, including cigarette smoke, silica exposure, and mineral oil have been associated in the literature with an increased risk of RA.
We have previously observed an elevated risk of RA in women living close to roadways and an association between exposure to certain air pollutants and risk for RA in a Swedish case-control study. Therefore we wanted to examine if adult exposures to air pollution were associated with increased risks for RA in the Nurses’ Health Study
Predicted ambient exposures to air pollution (SO2, NO2, and particulate matter) during adulthood were not associated with an increased risk of incident RA in this group of socially advantaged older women.
Acknowledgments
Supported by NIH grants R01 AR49880, CA87969, P60 AR047782, K24 AR0524-01 and P01 CA87969, ES017017
Footnotes
The authors have no conflicts of interest to declare
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