Abstract
Objective:
Rheumatoid Arthritis (RA) is postulated to originate at mucosal surfaces, particularly the airway mucosa. To investigate this hypothesis, we determined the association between RA and asthma, passive smoke exposure, and age of starting smoking.
Methods:
This case-control study identified 1,023 cases of RA (175 incident) within a single-center biobank population using a rules-based algorithm that combined self-report with two diagnosis codes. Exposures were self-reported on biobank questionnaires. Logistic regression models calculated the association of exposures with RA, adjusting for potential confounders.
Results:
After adjusting for allergies, urban environment, and passive smoke exposure, asthma was associated with RA in the full cohort (OR 1.28, 95% CI 1.04 to 1.58) but not the incident cohort (OR 1.17, 95% CI 0.66 to 2.06). History of allergic disease was also associated with RA in both the full (OR 1.30, 95% CI 1.12 to 1.51) and incident cohorts (OR 1.61, 95% CI 1.11 to 2.33), especially food allergy (OR 1.38, CI 1.08 to 1.75, and OR 1.83, 95% CI 0.97 to 3.45, respectively). Passive smoke exposure at home or work was not associated with RA. Finally, age of starting smoking was not associated with increased odds of developing RA in either the full (OR 1.03, 95% CI 1.00 to 1.06) or incident cohorts (OR 1.00, 95% CI 0.92 to 1.08).
Conclusion:
Asthma and allergies may be associated with increased risk of RA. Passive smoke exposure and earlier age of starting smoking do not appear to influence risk of RA.
Rheumatoid arthritis (RA) is one of the most common autoimmune diseases, affecting nearly 1 in 100 individuals (1). Seropositive RA in particular is hypothesized to originate from inflammation in the respiratory tract, resulting in autoantibody formation that later leads to disease (2, 3). However, several questions about this hypothesis of disease generation remain, which might help elucidate disease pathogenesis.
First, several studies show an association between asthma and RA, which may be explained by a shared immunologic mechanism (4–8). However, a major limitation of these previous studies is the lack of adjustment for allergic disease. Moreover, none have adjusted for second-hand cigarette smoke or urban pollution, which are known contributors both to RA (9–12) and to asthma (13, 14). It is unclear whether asthma is associated with RA after adjusting for these important confounders.
Second, the association between personal smoking and RA has been well-established (15, 16), yet only three studies have investigated the association between passive smoke exposure and RA (10, 17, 18). Results of these studies conflict, with two suggesting an association between passive smoking and RA, and the other showing no association. None of these prior studies characterized work smoke exposure alone or contained information about both the duration and intensity to allow a dose-response analysis. Earlier age of smoking may also be important but has not yet been studied in patients with RA (19).
Our aim was to clarify these gaps in knowledge related to the oral-respiratory factors that might mediate RA pathogenesis. Specifically, we aimed to determine the association of RA with asthma after controlling for allergic disease, urban environment, and passive smoke; passive smoke exposure both at home and work; and age of starting smoking. We hypothesized that the association with asthma would be attenuated after adjusting for allergy and environmental pollutants; that passive smoke at higher doses would be associated with RA; and that earlier age of smoking onset would be associated with an increased RA risk.
Patients and Methods
Study Design
This case-control study followed the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines for observational studies (20). It received approval from the Mayo Clinic and Olmsted County Institutional Review Boards (17–010806; 060-OMC-17) and complies with the Declaration of Helsinki.
Questionnaire data for this study came from the Mayo Clinic Biobank repository (21). Active recruitment occurred from April 2009 to December 2015 in both Minnesota and Florida. Eligibility criteria included age 18 or older, ability to communicate in English, capacity to consent, and residence in the United States. Approximately 29% of those invited chose to participate and completed a baseline questionnaire, yielding the 55,898 current participants. Of those, 77% completed the follow-up questionnaire sent approximately four years later. To ensure validity and quality of data, all questionnaires were visually examined for errors and omissions. Those with more than ten errors were returned to participants for correction. A computer program also evaluated for logical errors and incorrect skip patterns, flagging records for manual verification. Of the 55,898 biobank participants, approximately 87% were recruited from primary care locations, including internal medicine, preventive medicine, family medicine, and obstetrics/gynecology. Most of the remaining participants were recruited from orthopedics.
Participants
In this study, we defined cases of RA using a rules-based algorithm that combined self-reported RA on either the baseline or follow-up questionnaire with at least two RA diagnosis codes (714.0 or 714.9) 30 days apart. We excluded 38 participants who reported RA on the baseline questionnaire but did not report RA on the follow-up questionnaire as those participants are likely to have misrepresented RA status. Using a separate cohort of 732 people screened for inclusion in the RA cohort of the Rochester Epidemiology Project (which used the 1987 ACR classification criteria) (1), we found the positive predictive value (PPV) of this definition to be 88%. This accuracy was superior to diagnostic codes alone, which is a widely accepted method.
We also included 78 participants who had no RA self-report but three or more diagnosis codes for RA, with the first code occurring at least 90 days after the most recent survey (presumed incident RA). The incident RA group also included 97 RA cases who self-reported RA on the follow-up but not baseline questionnaire, for a total of 175 incident cases. To determine the accuracy of our RA definition in this full cohort, we performed manual chart verification using American College of Rheumatology 2010 criteria in a subset of 100 randomly-selected RA cases (22). VLK performed the main chart review, with JMD adjudicating any cases with uncertainty. Of the 100 patients, 87 were found to have RA or seronegative inflammatory arthritis clinically consistent with RA, for a PPV of 87% (see Table S1).
We defined the index date as the date of RA diagnosis. Date of RA diagnosis came from the earliest diagnostic code for RA provided this date was in the age range of RA diagnosis reported by the patient, or earlier. If the first diagnostic code occurred after the patient-reported date of RA diagnosis, then we used the patient-reported date of RA diagnosis.
Controls for this study included biobank participants without self-reported RA and no diagnosis codes for RA to ensure none had RA (negative predictive value > 99%). This study used 3:1 matching for the remaining biobank participants, with matching criteria consisting of recruitment year (within five years), recruitment location (Minnesota or Florida), distance from recruitment location (within 500 miles), age at baseline survey (within five years), and sex. The index date for each control was defined as the date of RA diagnosis for the corresponding case.
Measures
The pre-specified primary exposures of interest included self-reported asthma, presence of passive smoke exposure at home and work, and age of starting to smoke regularly. For passive smoke exposure, we also studied the age the smoke exposure started, years of exposure before index date, packs per day, and pack-years before index date. Home and work smoke exposure in packs per day was only available in the second version of the baseline questionnaire. Self-report is the most accurate way to study smoking history, and studies suggest that self-reported asthma has a high positive predictive value as well (23, 24). Nevertheless, we (VLK) performed a manual verification of self-reported asthma on a random subset of 50 study participants. We used previously published criteria to define “definite” asthma as a physician diagnosis of asthma during a clinical visit in the chart and/or if each of the following three conditions was present: (1) history of cough with wheezing or dyspnea, (2) variability in symptoms, and (3) at least two or more supportive measures including sleep disturbance, nonsmoker, nasal polyps, blood eosinophilia, elevated IgE, history of wheezing on exposure to antigen, asthma by pulmonary function test criteria, and/or favorable clinical response to bronchodilator (25). We defined “probable” asthma as having the first two conditions but not the third. Using these criteria, we found 82% to have definite asthma and 8% to have probable asthma (see Table S2).
History of allergy was another exposure of interest in this study. The questionnaire asked participants, “Do you have any allergies?” to which they responded yes or no. If yes, participants were prompted to mark all allergies that apply: “food allergies such as shellfish or nuts,” “grasses, pollen, or dust,” “pets,” “insect stings or bits,” and/or “other.”
Secondary exposures of interest included history of personal smoking (yes/no), current smoking (yes/no), smoking duration before index date (years), packs per day, and pack-years before index date. Data for all exposures and confounders of interest came from the baseline and follow-up questionnaires. Age, sex, body mass index (BMI), and race were supplemented by the Mayo Clinic medical record, leading to complete data for these fields (26). Obesity was defined as BMI ≥30 kg/m2.
Statistical Analysis
Chi square tests were used to compare proportions and Wilcoxon rank-sum tests were used to compare continuous variables. To assess the relationship between each exposure and case/control status, unconditional logistic regression models were used. Sensitivity analyses were performed among the 175 cases of incident RA to allow for interpretation of results with clear timing of exposures before disease and without any possibility of recall bias. An additional sensitivity analysis was performed in the subset of 189 residents with known serologic status, of whom 111 were positive for antibodies to rheumatoid factor (RF) and/or cyclic citrullinated peptide (CCP).
Of the 4,084 study participants, 322 (7.8%) were missing data for at least one of the key questionnaire items, including history of asthma; allergy; and personal, home, or work smoking exposure. Because of the low frequency of missing data and even lower missingness among each of the individual models, the impact of missing data on the main study results was deemed low. Thus, participants with missing data were excluded from the model pertaining to that exposure. All analyses were pre-specified in a protocol (see supplementary material) unless described as “post hoc.” We performed analyses using SAS version 9.4 (SAS Institute Inc., Cary, NC), with a significance threshold of two-sided alpha = 0.05 and 95% confidence intervals (CI).
Results
This study included 1,023 cases of RA from the 55,898 biobank participants. Mean age at index date of RA diagnosis for both cases and controls was 50 years (standard deviation [SD] 16), while the mean age at the time of the baseline survey was 62 years (SD 13). In unadjusted analyses, the RA group a higher proportion of personal smoking and allergies, along with increased home smoke exposure pack-years compared to controls (Table 1). Characteristics of the incident RA subgroup were similar to the full RA cohort except for slightly longer duration of smoking before RA and lower frequency of work smoke exposure and asthma (Table 1).
Table 1.
Characteristics of RA cases and controls at index date of diagnosis
| Number (%), Mean (±SD), or Median (IQR) | |||
|---|---|---|---|
| Characteristic | RA cases (N = 1023) | Incident RA Subset (N = 175) | Controls (N = 3061) |
| Age, years | 50 (±16) | 63 (±14) | 50 (±16) |
| Female sex | 741 (72) | 119 (68) | 2215 (72) |
| Body mass index, kg/m^2* | 30 (±7) | 30 (±6) | 29 (±6) |
| Race: white, non-hispanic | 1000 (98) | 172 (98) | 2982 (97) |
| Education | |||
| Less than high school | 21 (2) | 1 (0.6) | 61 (2) |
| High school degree | 181 (18) | 24 (14) | 496 (16) |
| Technical school | 111 (11) | 20 (11) | 272 (9) |
| Some college | 281 (28) | 46 (26) | 722 (24) |
| Bachelor’s degree | 213 (21) | 37 (21) | 732 (24) |
| Graduate degree | 201 (20) | 43 (25) | 736 (24) |
| Other | 15 (2) | 4 (2) | 42 (1) |
| Personal smoking | |||
| Never | 532 (52) | 91 (52) | 1796 (59) |
| Past | 431 (42) | 77 (44) | 1105 (36) |
| Current* | 60 (6) | 7 (4) | 160 (5) |
| Duration of smoking | 22 (13,31) | 26 (12,23) | 18 (10,28) |
| Packs per day | 0.8 (0.5,1.5) | 0.8 (0.5,0.8) | 0.8 (0.5,0.8) |
| Pack-years | 15 (8,29) | 19 (10,35) | 13 (6,24) |
| Age started smoking regularly | 18 (16,19) | 18 (16,20) | 18 (16,20) |
| Asthma* | 169 (17) | 21 (13) | 383 (13) |
| Urban environment | 741 (73) | 124 (72) | 2260 (74) |
| Any allergy* | 643 (64) | 112 (65) | 1739 (58) |
| Food | 106 (11) | 17 (10) | 241 (8) |
| Grasses, pollen, or dust | 323 (32) | 56 (33) | 932 (31) |
| Pets | 125 (12) | 19 (11) | 360 (12) |
| Insects | 76 (8) | 11 (6) | 228 (8) |
| Other | 380 (38) | 67 (39) | 957 (32) |
| Home smoke exposure | |||
| Yes | 665 (65) | 104 (60) | 1890 (62) |
| Age home smoke exposure started | 0 (0,15) | 0 (0,15) | 0 (0,15) |
| Duration | 18 (12,21) | 20 (16,29) | 18 (10,20) |
| Packs per day | 0.8 (0.5,1.5) | 0.8 (0.5,1.5) | 0.8 (0.5,1.5) |
| Pack-years | 14 (8,27) | 17 (9,29) | 12 (8,26) |
| Work smoke exposure | |||
| Yes | 394 (39) | 62 (35) | 1105 (36) |
| Age work smoke exposure started | 25 (17,25) | 25 (17,25) | 25 (17,25) |
| Duration | 10 (5,20) | 10 (5,20) | 10 (5,19) |
| Packs per day | 0.8 (0.5,1.5) | 0.8 (0.5,1.5) | 0.8 (0.5,1.5) |
| Pack-years | 8 (4,15) | 10 (4,20) | 8 (3,15) |
| Dental visit in previous year* | 863 (85) | 154 (88) | 2646 (87) |
At time of baseline questionnaire rather than index date
SD=standard deviation, IQR=interquartile range, RA = rheumatoid arthritis
In the full study cohort in the unadjusted analysis, asthma was significantly associated with RA (odds ratio [OR] 1.38, 95% CI 1.14 to 1.68). This difference remained statistically significant even after controlling for home and work smoke exposure, urban environment, and presence of allergies (OR 1.28, 95% CI 1.04 to 1.58). However, it was not statistically significant in the incident RA subset (OR 1.17, 95% CI 0.66 to 2.06) (Table 2).
Table 2.
Association between asthma and RA
| Odds Ratio (95% Cl) | ||
|---|---|---|
| Adjustment Type | All RA Cases (N = 1023) | Incident RA Subset (N = 175) |
| Unadjusted | 1.38 (1.14,1.68) | 1.28 (0.75,2.20) |
| Standard adjustment* | 1.37 (1.12,1.67) | 1.31 (0.74,2.28) |
| Full adjustment** | 1.28 (1.04,1.58) | 1.17 (0.66,2.06) |
Bold values are statistically significant.
adjusting for age, sex, body mass index, race, education, personal smoking (never/past/current)
also adjusting for allergic disease, urban environment, and home/work smoke exposure
CI = confidence interval, RA = rheumatoid arthritis
History of any allergy was also associated with RA in the full study cohort (OR 1.30, 95% CI 1.12 to 1.51). Further analyses examined the association among different allergy types. Food allergies in particular were the most highly associated (OR 1.38, 95% CI 1.08 to 1.75) (Figure 1). The association with allergy was also significant in the incident RA subset (OR 1.61, 95% CI 1.11 to 2.33). In particular, history of food allergy was associated with a non-statistically significant increase in the odds of RA (OR 1.83, 95% CI 0.97 to 3.45).
Figure 1: Adjusted association between allergy types and RA.
Each data point represents a separate model adjusting for age, sex, body mass index, race, education, and personal smoking (never/past/current). Reference is participants without the particular allergy. Bars represent 95% confidence intervals.
There was no evidence of an association between home or work smoke exposure and RA in either the full cohort or the incident RA subset (Table 3). When home and work smoke exposure were combined in a post hoc analysis, participants with the highest levels of cumulative passive smoke exposure had increased odds of developing RA in the full cohort (OR 1.37, 95% CI 1.02 to 1.84) but not in the incident RA subset (OR 1.34, 95% CI 0.68 to 2.67; Table 3). To determine whether the effect of passive smoke exposure might be higher among non-smokers, we performed an additional analysis examining the interaction between history of personal smoking (yes or no) and pack-years of home, work, and combined home/work smoke exposure. There was no evidence that passive smoking had a different effect among non-smokers compared to smokers (p-value for interaction = 0.29, 0.65, and 0.67, respectively).
Table 3.
Adjusted association between passive smoke exposure and RA*
| Multivariable OR (95% CI) | ||
|---|---|---|
| Passive Smoke Type | All RA Cases (N = 1023) | Incident RA Subset (N = 175) |
| Home smoke exposure | ||
| Yes | 1.06 (0.91,1.23) | 0.81 (0.56,1.18) |
| Age home exposure started** | 1.00 (0.99,1.01) | 0.99 (0.96,1.01) |
| Duration, years† | 1.09 (0.99,1.21) | 1.37 (1.08,1.72) |
| Packs per day | 1.12 (0.94,1.01) | 1.20 (0.74,1.94) |
| Pack-years† | 1.03 (0.97,1.09) | 1.14 (0.99,1.32) |
| None (reference) | 1.00 | 1.00 |
| 1–9 | 0.97 (0.78,1.21) | 0.63 (0.35,1.17) |
| 10–19 | 1.13 (0.88,1.45) | 0.83 (0.43,1.62) |
| 20–29 | 1.27 (0.94,1.71) | 1.41 (0.68,2.92) |
| 30–39 | 1.13 (0.71,1.78) | 1.07 (0.35,3.34) |
| 40+ | 1.14 (0.80,1.63) | 1.09 (0.47,2.51) |
| Work smoke exposure | ||
| Yes | 1.01 (0.86,1.17) | 1.04 (0.71,1.52) |
| Age work exposure started** | 1.01 (0.99,1.03) | 1.03 (0.97,1.07) |
| Duration, years† | 1.01 (0.85,1.19) | 1.05 (0.66,1.67) |
| Packs per day | 1.15 (0.97,1.36) | 1.20 (0.74,1.93) |
| Pack-years† | 1.04 (1.00,1.09) | 1.12 (0.83,1.49) |
| None (reference) | 1.00 | 1.00 |
| 1–9 | 1.01 (0.81,1.26) | 0.94 (0.51,1.74) |
| 10–19 | 0.88 (0.61,1.25) | 0.92 (0.38,2.21) |
| 20–29 | 0.84 (0.46,1.52) | 1.12 (0.21,6.09) |
| 30–39 | 1.80 (1.01,3.19) | 2.36 (0.56,9.94) |
| 40+ | 1.17 (0.64,2.13) | 3.55 (0.64,19.8) |
| Combined home/work smoke exposure | ||
| Pack-years† | 1.04 (1.00,1.09) | 1.15 (1.02,1.29) |
| None (reference) | 1.00 | 1.00 |
| 1–9 | 0.93 (0.74,1.17) | 0.65 (0.35,1.21) |
| 10–19 | 1.08 (0.84,1.39) | 0.63 (0.31,1.26) |
| 20–29 | 1.07 (0.79,1.45) | 0.96 (0.46,2.01) |
| 30–39 | 1.13 (0.76,1.67) | 1.07 (0.31,3.76) |
| 40+ | 1.37 (1.02,1.84) | 1.34 (0.68,2.67) |
each is a separate model adjusting for age, sex, body mass index, race, education, personal smoking (never/past/current). Bold values are statistically significant.
also adjusting for the pack-years of passive smoke exposure
Odds ratios reported per 10 units
CI = confidence interval, OR = odds ratio, RA = rheumatoid arthritis
Personal smoking, including duration, intensity, and pack-years, were all significant predictors of RA in the full cohort but not in the incident RA subset (Table 4). Age of starting smoking was not associated with an increase in the odds of developing RA in either the full cohort (OR 1.03, 95% CI 1.00 to 1.06) or incident cohort (OR 1.00, 95% CI 0.92 to 1.08).
Table 4.
Adjusted association between personal smoking and RA*
| Multivariable OR (95% CI) | ||
|---|---|---|
| All RA Cases (N = 1023) | Incident RA Subset (N = 175) | |
| Ever smoked (vs. never) | 1.26 (1.09,1.46) | 1.26 (0.88,1.81) |
| Current smoker (vs. past/never) | 1.07 (0.78,1.46) | 0.68 (0.29,1.59) |
| Duration, years† | 1.18 (1.06,1.31) | 0.97 (0.77,1.22) |
| Packs per day | 1.20 (1.03,1.40) | 1.06 (0.71,1.58) |
| Pack-years† | 1.07 (1.01,1.13) | 0.96 (0.84,1.10) |
each is a separate model adjusting for age, sex, body mass index, race, education. Bold values are statistically significant.
Odds ratios reported per 10 units
CI = confidence interval, OR = odds ratio, RA = rheumatoid arthritis, vs = versus
Finally, a sensitivity analysis was performed among the subset of patients known to have elevation in RF and/or CCP antibodies. Although this antibody positive group was small, associations were similar to the main results of the study (see Table S3). However, there was a suggestion of a stronger association with grass allergy and home smoke exposure (yes or no). Characteristics of patients with missing data are shown in Table S4.
Discussion
This study addresses several gaps related to the oral-respiratory hypothesis of RA pathogenesis. Namely, it supports a marginal association between asthma and RA, identifies a connection between allergy and RA, provides evidence against passive smoke or earlier age of smoking predisposing to RA.
First, in the full cohort, asthma was associated with RA even after adjusting for allergy and pollutants, an association not previously reported. The association between asthma and RA is potentially biologically plausible. Both are immunological disorders profoundly influenced by environmental factors that induce oxidative stress such as cigarette smoke, wood smoke, and environmental pollution (9–14). Moreover, there are many potential mechanisms that may mediate both disorders, including Th17 inflammation, infectious triggers, premature immune senescence, or other inflammatory mediators, such as TNF-alpha and leukotrienes (27). However, the strength of association did attenuate after adjusting for important new confounders, and the relationship was not statistically significant in the incident RA cohort. Thus, the overall findings from this study reflect the uncertainty of existing literature, with some studies supporting an association between asthma and RA (4–8, 28–30) and others refuting such an association (23, 31–35).
On the other hand, the presence of a positive association between allergy and RA was initially unexpected since current immunologic theory segregates RA into the Th1 pathway and allergy into the Th2 pathway (36). A review of older literature shows that the association between allergy and RA was such a strong clinical observation that rheumatologists thought allergy might be involved in the pathogenesis of RA (37, 38). Subsequently, several small studies showed no association between atopy and RA (31–33, 39, 40). More recently, however, several larger cohort studies have repeatedly found a positive association between allergic disease and RA (5, 28, 41–43), and a study in the Netherlands showed that parental autoimmunity increased the odds of offspring allergic disease (44). Biological support for this association includes higher levels of IgE antibodies (45), IL-4 production (46), and mast cells (47) in patients with RA. It is possible that like RA and asthma, a broader problem with immune dysregulation underlies both types of diseases, and having one predisposes to the other.
Passive smoke exposure at home or work, including the age the exposure began, duration, intensity, and pack-years was not associated with RA. A prior study also found no evidence of association when combining work and home exposure together (17). However, that study did not contain information about the intensity of smoke exposure, so it could not evaluate the dose-response effect. When stratifying passive smoke exposure by pack-years, we did find evidence of an association between RA and the highest levels of work smoke exposure and combined home and work smoke exposure. Thus, high levels of passive smoke exposure may place individuals at higher risk for RA, even among smokers. This finding may explain why the two other studies of passive smoking suggested an association with RA, especially among people with a longer duration of exposure extending back to childhood (10, 18).
Personal smoking was also associated with RA compared to non-smoking in the full cohort. This is consistent with previous studies (10, 16), which provides reassurance about the validity of this study. However, personal smoking was not associated with RA in the incident cohort. There are several potential explanations for this. One is that the incident RA cohort reflects a different subtype of RA compared to older cases with disease etiology rooted in factors other than smoking. Alternatively, it is possible that the incident RA group has more misclassification than the full RA cohort, biasing its results to the null. Thus, it is important to consider both the full RA cohort and the incident cohort when interpreting study findings. A novel finding related to personal smoking history was that earlier age of smoking was not associated with increased odds of RA. Thus, starting smoking earlier in life may not be more dangerous compared to initiating smoking later in life with respect to RA risk.
One strength of this study is its large sample size. Another is its detailed questionnaire data. These detailed survey questions provided impressive granularity for both the exposures of interest such as personal and passive smoking, and also potential confounders including allergies, urban environment, and educational background. Future studies can leverage not only this questionnaire data, but also the associated lifestyle, clinical, genetic, and serological data contained within this rich and novel dataset (21).
There are also several important limitations to consider. First, using a convenience sample from clinics rather than a population-based study creates potential selection bias and limits generalizability, even when the population comes mainly from primary care as in this study. Second, recall bias is possible when asking patients about past exposures. However, participants were asked about all comorbidities and exposures both related and unrelated to RA, and the similarity of the incident RA group results (which did not have RA at the time of the survey) provides some reassurance. Third, the definition of RA relied partially on self-report, creating potential misclassification bias. Nevertheless, such bias would have reduced any observed associations, and the similarity with incident RA cases was reassuring. Furthermore, the high PPV found in the two verification methods provides further reassurance about the rules-based definition of RA used in this study. Finally, many study participants did not have data on RA autoantibody status, limiting the number of calculations that could be performed in the sensitivity analysis. Notably, Hedstrom et al. showed no difference in the association between passive smoke and RA by antibody status (17).
In conclusion, asthma and allergies may be associated with increased risk of developing RA, but passive smoke exposure and age of smoking initiation are not. Future studies investigating the relationship between RA and early life atopy are needed.
Supplementary Material
Acknowledgements:
The Mayo Clinic Center for Individualized Medicine
Financial Support: Funding for this project was provided by the Rheumatology Research Foundation Resident Research Preceptorship
Footnotes
Conflicts of Interest: None
References:
- 1.Myasoedova E, Crowson CS, Kremers HM, Therneau TM, Gabriel SE. Is the incidence of rheumatoid arthritis rising?: results from Olmsted County, Minnesota, 1955–2007. Arthritis Rheum. 2010;62(6):1576–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Janssen KM, de Smit MJ, Brouwer E, de Kok FA, Kraan J, Altenburg J, et al. Rheumatoid arthritis-associated autoantibodies in non-rheumatoid arthritis patients with mucosal inflammation: a case-control study. Arthritis Res Ther. 2015;17:174. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Quirke AM, Perry E, Cartwright A, Kelly C, De Soyza A, Eggleton P, et al. Bronchiectasis is a Model for Chronic Bacterial Infection Inducing Autoimmunity in Rheumatoid Arthritis. Arthritis Rheumatol. 2015;67(9):2335–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Sheen YH, Rolfes MC, Wi CI, Crowson CS, Pendegraft RS, King KS, et al. Association of Asthma with Rheumatoid Arthritis: A Population-Based Case-Control Study. J Allergy Clin Immunol Pract. 2018;6(1):219–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Lai NS, Tsai TY, Koo M, Lu MC. Association of rheumatoid arthritis with allergic diseases: A nationwide population-based cohort study. Allergy Asthma Proc. 2015;36(5):99–103. [DOI] [PubMed] [Google Scholar]
- 6.Hemminki K, Li X, Sundquist J, Sundquist K. Subsequent autoimmune or related disease in asthma patients: clustering of diseases or medical care? Ann Epidemiol. 2010;20(3):217–22. [DOI] [PubMed] [Google Scholar]
- 7.de Roos AJ, Cooper GS, Alavanja MC, Sandler DP. Personal and family medical history correlates of rheumatoid arthritis. Ann Epidemiol. 2008;18(6):433–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hassan WU, Keaney NP, Holland CD, Kelly CA. Bronchial reactivity and airflow obstruction in rheumatoid arthritis. Ann Rheum Dis. 1994;53(8):511–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Hart JE, Laden F, Puett RC, Costenbader KH, Karlson EW. Exposure to traffic pollution and increased risk of rheumatoid arthritis. Environ Health Perspect. 2009;117(7):1065–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Costenbader KH, Feskanich D, Mandl LA, Karlson EW. Smoking intensity, duration, and cessation, and the risk of rheumatoid arthritis in women. Am J Med. 2006;119(6):503.e1–9. [DOI] [PubMed] [Google Scholar]
- 11.Chang KH, Hsu CC, Muo CH, Hsu CY, Liu HC, Kao CH, et al. Air pollution exposure increases the risk of rheumatoid arthritis: A longitudinal and nationwide study. Environ Int. 2016;94:495–9. [DOI] [PubMed] [Google Scholar]
- 12.De Roos AJ, Koehoorn M, Tamburic L, Davies HW, Brauer M. Proximity to traffic, ambient air pollution, and community noise in relation to incident rheumatoid arthritis. Environ Health Perspect. 2014;122(10):1075–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Burke H, Leonardi-Bee J, Hashim A, Pine-Abata H, Chen Y, Cook DG, et al. Prenatal and passive smoke exposure and incidence of asthma and wheeze: systematic review and meta-analysis. Pediatrics. 2012;129(4):735–44. [DOI] [PubMed] [Google Scholar]
- 14.Guan WJ, Zheng XY, Chung KF, Zhong NS. Impact of air pollution on the burden of chronic respiratory diseases in China: time for urgent action. Lancet. 2016;388(10054):1939–51. [DOI] [PubMed] [Google Scholar]
- 15.Chang K, Yang SM, Kim SH, Han KH, Park SJ, Shin JI. Smoking and rheumatoid arthritis. Int J Mol Sci. 2014;15(12):22279–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Di Giuseppe D, Discacciati A, Orsini N, Wolk A. Cigarette smoking and risk of rheumatoid arthritis: a dose-response meta-analysis. Arthritis Res Ther. 2014;16(2):R61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Hedstrom AK, Klareskog L, Alfredsson L. Exposure to passive smoking and rheumatoid arthritis risk: results from the Swedish EIRA study. Ann Rheum Dis. 2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Seror R, Henry J, Gusto G, Aubin H, Boutron-Ruault M, Mariette X. Passive smoking in childhood increases risk of developing rheumatoid arthritis. Rheumatology. 2018. [DOI] [PubMed] [Google Scholar]
- 19.Sparks JA, Chang SC, Deane KD, Gan RW, Kristen Demoruelle M, Feser ML, et al. Associations of Smoking and Age With Inflammatory Joint Signs Among Unaffected First-Degree Relatives of Rheumatoid Arthritis Patients: Results From Studies of the Etiology of Rheumatoid Arthritis. Arthritis Rheumatol. 2016;68(8):1828–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. Int J Surg. 2014;12(12):1495–9. [DOI] [PubMed] [Google Scholar]
- 21.Olson JE, Ryu E, Johnson KJ, Koenig BA, Maschke KJ, Morrisette JA, et al. The Mayo Clinic Biobank: a building block for individualized medicine. Mayo Clin Proc. 2013;88(9):952–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham CO 3rd, et al. 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum. 2010;62(9):2569–81. [DOI] [PubMed] [Google Scholar]
- 23.Sparks JA, Lin TC, Camargo CA Jr., Barbhaiya M, Tedeschi SK, Costenbader KH, et al. Rheumatoid arthritis and risk of chronic obstructive pulmonary disease or asthma among women: A marginal structural model analysis in the Nurses’ Health Study. Semin Arthritis Rheum. 2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Oksanen T, Kivimaki M, Pentti J, Virtanen M, Klaukka T, Vahtera J. Self-report as an indicator of incident disease. Ann Epidemiol. 2010;20(7):547–54. [DOI] [PubMed] [Google Scholar]
- 25.Bang DW, Wi CI, Kim EN, Hagan J, Roger V, Manemann S, et al. Asthma Status and Risk of Incident Myocardial Infarction: A Population-Based Case-Control Study. J Allergy Clin Immunol Pract. 2016;4(5):917–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Chute CG, Beck SA, Fisk TB, Mohr DN. The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data. J Am Med Inform Assoc. 2010;17(2):131–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Kamradt T Can infections prevent or cure allergy and autoimmunity? Discov Med. 2005;5(27):283–7. [PubMed] [Google Scholar]
- 28.Jeong HE, Jung SM, Cho SI. Association between Rheumatoid Arthritis and Respiratory Allergic Diseases in Korean Adults: A Propensity Score Matched Case-Control Study. Int J Rheumatol. 2018;2018:3798124. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Kero J, Gissler M, Hemminki E, Isolauri E. Could TH1 and TH2 diseases coexist? Evaluation of asthma incidence in children with coeliac disease, type 1 diabetes, or rheumatoid arthritis: a register study. J Allergy Clin Immunol. 2001;108(5):781–3. [DOI] [PubMed] [Google Scholar]
- 30.Provenzano G, Donato G, Brai G, Rinaldi F. Prevalence of allergic respiratory diseases in patients with RA. Ann Rheum Dis. 2002;61(3):281. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Hartung AD, Bohnert A, Hackstein H, Ohly A, Schmidt KL, Bein G. Th2-mediated atopic disease protection in Th1-mediated rheumatoid arthritis. Clin Exp Rheumatol. 2003;21(4):481–4. [PubMed] [Google Scholar]
- 32.Rudwaleit M, Andermann B, Alten R, Sorensen H, Listing J, Zink A, et al. Atopic disorders in ankylosing spondylitis and rheumatoid arthritis. Ann Rheum Dis. 2002;61(11):968–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Olsson AR, Wingren G, Skogh T, Svernell O, Ernerudh J. Allergic manifestations in patients with rheumatoid arthritis. APMIS. 2003;111(10):940–4. [DOI] [PubMed] [Google Scholar]
- 34.Sheikh A, Smeeth L, Hubbard R. There is no evidence of an inverse relationship between TH2-mediated atopy and TH1-mediated autoimmune disorders: Lack of support for the hygiene hypothesis. J Allergy Clin Immunol. 2003;111(1):131–5. [DOI] [PubMed] [Google Scholar]
- 35.Bergstrom U, Jacobsson LT, Nilsson JA, Berglund G, Turesson C. Pulmonary dysfunction, smoking, socioeconomic status and the risk of developing rheumatoid arthritis. Rheumatology (Oxford). 2011;50(11):2005–13. [DOI] [PubMed] [Google Scholar]
- 36.Singh VK, Mehrotra S, Agarwal SS. The paradigm of Th1 and Th2 cytokines: its relevance to autoimmunity and allergy. Immunol Res. 1999;20(2):147–61. [DOI] [PubMed] [Google Scholar]
- 37.Iakovleva AA. [THE ROLE OF ALLERGY IN THE PATHOGENESIS OF RHEUMATOID ARTHRITIS]. Sov Med. 1964;27:29–36. [PubMed] [Google Scholar]
- 38.Ragan C Role of hypersensitivity in the pathogenesis of rheumatoid arthritis. Ann Rheum Dis. 1959;18(1):1–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.O’Driscoll BR, Milburn HJ, Kemeny DM, Cochrane GM, Panayi GS. Atopy and rheumatoid arthritis. Clin Allergy. 1985;15(6):547–53. [DOI] [PubMed] [Google Scholar]
- 40.Hilliquin P, Allanore Y, Coste J, Renoux M, Kahan A, Menkes CJ. Reduced incidence and prevalence of atopy in rheumatoid arthritis. Results of a case-control study. Rheumatology (Oxford). 2000;39(9):1020–6. [DOI] [PubMed] [Google Scholar]
- 41.Hou YC, Hu HY, Liu IL, Chang YT, Wu CY. The risk of autoimmune connective tissue diseases in patients with atopy: A nationwide population-based cohort study. Allergy Asthma Proc. 2017;38(5):383–9. [DOI] [PubMed] [Google Scholar]
- 42.Karsh J, Chen Y, Lin M, Dales R. The association between allergy and rheumatoid arthritis in the Canadian population. Eur J Epidemiol. 2005;20(9):783–7. [DOI] [PubMed] [Google Scholar]
- 43.Simpson CR, Anderson WJ, Helms PJ, Taylor MW, Watson L, Prescott GJ, et al. Coincidence of immune-mediated diseases driven by Th1 and Th2 subsets suggests a common aetiology. A population-based study using computerized general practice data. Clin Exp Allergy. 2002;32(1):37–42. [DOI] [PubMed] [Google Scholar]
- 44.Maas T, Nieuwhof C, Passos VL, Robertson C, Boonen A, Landewe RB, et al. Transgenerational occurrence of allergic disease and autoimmunity: general practice-based epidemiological research. Prim Care Respir J. 2014;23(1):14–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Burastero SE, Lo Pinto G, Goletti D, Cutolo M, Burlando L, Falagiani P. Rheumatoid arthritis with monoclonal IgE rheumatoid factor. J Rheumatol. 1993;20(3):489–94. [PubMed] [Google Scholar]
- 46.Frieri M. Neuroimmunology and inflammation: implications for therapy of allergic and autoimmune diseases. Ann Allergy Asthma Immunol. 2003;90(6 Suppl 3):34–40. [DOI] [PubMed] [Google Scholar]
- 47.Xu Y, Chen G. Mast cell and autoimmune diseases. Mediators Inflamm. 2015;2015:246126. [DOI] [PMC free article] [PubMed] [Google Scholar]
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