Abstract
Objective:
Since comorbidities such as autoimmune diseases may be associated with RA risk, we hypothesized that family history of these other conditions might also predict RA. Therefore, we aimed to determine the association between family history of 79 comorbidities and RA.
Methods:
This case-control study identified 821 cases of RA in the Mayo Clinic Biobank (positive predictive value 95%) and matched three controls to each case based on age, sex, recruitment year, and location. Family history and adjustors were self-reported. Logistic regression estimated odds ratios (OR) and confidence intervals (CI) for RA risk by presence of family history for each comorbidity, adjusted for body mass index, race, and smoking.
Results:
Family history of several conditions were associated with developing RA, including rheumatologic autoimmune diseases (adjusted OR [aOR] 1.89, 95% CI 1.41,2.52), pulmonary fibrosis (aOR 2.12, 95% CI 1.16,3.80), inflammatory bowel disease (aOR 1.45, 95% CI 1.05,1.98), hyper/hypothyroidism (aOR 1.34, 95% CI 1.10,1.63), and obstructive sleep apnea (aOR 1.28, 95% CI 1.05,1.55). Parkinson’s disease and type 2 diabetes were associated with a statistically decreased risk of RA that did not reach the pre-specified significance threshold of p<0.01 (aOR 0.70, 95% CI 0.49,0.98; aOR 0.81, 95% CI 0.67,0.97). Analyses among 143 cases of incident RA were similar and also suggested an association with family history of autism (OR 10.5, 95% CI 2.51,71.3).
Conclusion:
Family history of several autoimmune and non-autoimmune comorbidities were associated with increased risk of RA, providing opportunity to identify novel populations at risk for RA.
Family history of rheumatoid arthritis (RA) is known to be an important predictor of RA (1–9), even after accounting for genetic and environmental factors (10, 11). Supporting these findings, studies have shown the heritability of RA is estimated to be 50–60% (6, 12). Further, first degree relatives of RA patients have increased likelihood of having inflammatory joint signs (13), reactivity to multiple anti-citrullinated protein/peptide antibodies (14), and levels of multiple cytokines compared to those from a non-immune background (15). Identifying individuals at higher risk for RA is important because they are candidates for predictive testing (16, 17) and education interventions (18, 19).
It is plausible that family history of other comorbidities might also predispose to RA due to overlapping heritability or disease mechanisms, helping to identify those at higher risk for RA. For example, one study suggested that family history of other rheumatologic autoimmune diseases like lupus and connective tissue disease predicted RA (20). Other studies have shown an association not only between family history of rheumatologic autoimmune diseases and RA, but even other autoimmune diseases such as type 1 diabetes, thyroid disease, inflammatory bowel disease (IBD), and multiple sclerosis (6, 21, 22). However, these studies did not adjust for potential confounders such as smoking. It is also unknown whether family history of other comorbidities, including non-autoimmune comorbidities, increases risk of RA. Investigating this question may prove valuable, as several non-autoimmune conditions have been associated with RA (23–26).
To address these gaps, we performed a study leveraging the Mayo Clinic Biobank (27). Our aims were twofold. First, we aimed to determine the association between family history of autoimmune diseases and RA. Second, we aimed to define the association between family history of non-autoimmune diseases and RA. We hypothesized that family history of immune-mediated conditions such as autoimmune diseases and cancers would predispose to developing RA.
PATIENTS AND METHODS
Study Design and Participants
This case-control study used the Mayo Clinic Biobank’s 55,898 participants, recruited from 2009 to present, and their self-reported questionnaire data (27). Participants were recruited mainly from primary care locations and filled out the questionnaire at the time of enrollment, either at home or in the clinic. The questionnaire asked participants to report general health, personal and family medical history, health behaviors, and environmental exposures (see online supplementary material). All questionnaires were manually and electronically examined for errors, omissions, and skip patterns, and returned to participants for correction if there were more than ten errors. Additional details regarding these participants and the questionnaires have been described elsewhere (28).
This study received approval from the Mayo Clinic and Olmsted County Institutional Review Boards and complies with the Declaration of Helsinki. All participants provided written informed consent. This manuscript also follows the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) reporting guidelines for observational studies (29).
RA Cases
We identified 821 RA cases using a rules-based algorithm combining two occurrences of RA diagnosis codes (714.0 or 714.9) at least 30 days apart with use of a disease-modifying antirheumatic drug (DMARD) (30). DMARD data became available in the electronic health record (EHR) in 1999. As previously published, this definition had a positive predictive value (PPV) of 95% for RA by American College of Rheumatology/European League Against Rheumatism 2010 criteria (28, 31). Of the identified cases of RA, 143 (17%) had no RA self-report on the baseline or follow-up questionnaire but met the criteria for RA based on two diagnosis codes and a DMARD at least 90 days after the most recent questionnaire (defined as “incident RA”). Seropositive RA was defined as positivity for either rheumatoid factor (RF) or cyclic citrullinated peptide (CCP) antibodies. This antibody data became available in the EHR in 1987.
Controls
Eligible controls were participants without self-reported RA or diagnosis codes for RA in the EHR as of February 28, 2018. We matched each RA case to three controls at the index date of the baseline questionnaire. Matching factors as of index date were age (within 5 years), sex, year (within five years), recruitment location (Minnesota or Florida), and distance from recruitment location (within 500 miles).
Family History of Comorbidities
Family history for 79 comorbidities was self-reported on the Biobank’s baseline questionnaire. As worded on the questionnaire, these comorbidities included RA; autoimmune diseases such as rheumatologic autoimmune disorder, Crohn’s disease or ulcerative colitis, celiac disease, and hyperthyroidism/hypothyroidism; 22 types of cancer including thyroid, lung, breast, esophageal, pancreatic, stomach, colon or rectal, liver, uterine/endometrial, cervical, ovarian, prostate, testicular, bone, kidney, urinary/bladder, melanoma, nonmelanoma skin cancer, sarcoma, leukemia, lymphoma, and other cancer; and non-autoimmune diseases such as osteoarthritis (OA) and obstructive sleep apnea (OSA), among others (see questionnaire in the online supplementary material for a complete list of all comorbidities). Of note, the “rheumatologic autoimmune disorder” comorbidity and prompted participants with the examples of lupus and scleroderma.
Family history came from the question, “Do or did any of your first-degree relatives (parents, sisters, brothers, children) have this condition?” Participants responded “yes,” “no,” or “don’t know” for each comorbidity. For the primary analysis, responses of “don’t know” were assumed not to have a family member with that particular comorbidity, and were coded as “no.” The proportion missing each family history item ranged from 7 to 13%.
Covariates
Potential confounding variables in this study included age, sex, body mass index (BMI) (continuous), race (non-Hispanic white versus all others), education (bachelor’s degree or higher versus all others), and smoking status (ever versus never). Race, education, and smoking were self-reported on the questionnaire. EHR data provided sex, age and BMI at the index date of baseline questionnaire, along with missing smoking status within one year of index date. Family characteristics such as adoption status, parental age of death, and number of siblings and children were self-reported on the questionnaire.
Statistical Analysis
Chi square tests compared proportions between RA cases and controls, while Wilcoxon rank-sum tests compared continuous variables. Unadjusted odds ratios for RA case status were calculated for all family history items. Unconditional logistic regression models were used to obtain adjusted odds ratios (ORs) with 95% confidence intervals (CI) for comorbidities with two-sided p<0.05 in unadjusted comparisons. Adjustors included age, sex, BMI, race, education, and smoking. The exposure variable was family history of each comorbidity in a first-degree relative (present versus absent), and the outcome variable was RA case status. We also tested each model for an interaction between family history and educational level, since RA is associated with lower education level. To account for the multiple comparisons in this study without the overcorrection associated with methods such as the Bonferroni adjustment (32), we pre-selected a final significance threshold of two-sided p<0.01 before beginning the analysis.
Any possibly significant associations (unadjusted p<0.05) were also investigated in four sensitivity analyses. The first calculated associations among the 143 incident RA cases and their corresponding controls to minimize RA-associated recall bias. The second investigated associations among 250 RA cases with known serological status (positive versus negative). Sample size was too small to permit adjusting for confounders in these two sensitivity analyses. The third sensitivity analysis repeated all the analyses for the study categorizing “don’t know” responses as missing. A fourth sensitivity analysis used participants with type 2 diabetes as the comparison group to lessen the possible effects of illness-related recall bias. Participants with missing data for a particular family history item were excluded from that model. All analyses were pre-specified in a protocol and were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC).
RESULTS
Participant Characteristics
We identified 821 RA cases and 2,455 matched controls (Table 1). RA cases and controls also had no difference in family characteristics such as adoption status (p=0.31), parental age of death (p=0.89 and 0.10 for fathers and mothers, respectively), number of siblings (p=0.66 for brothers, p = 0.41 for sisters), or number of children (p=0.46 for sons, p=0.97 for daughters). Characteristics of the 143 RA cases who developed RA after the baseline questionnaire (incident RA) were similar to the overall RA group.
Table 1.
Demographic characteristics of RA cases and controls at baseline questionnaire
| Number (%) | |||
|---|---|---|---|
| Characteristic | RA Cases (N = 821) | Controls (N = 2455) | p-value |
| Age, years (mean ±SD) | 62 (±14) | 62 (±14) | 0.97 |
| Age at RA diagnosis (or matched date) | 50 (±16) | 50 (±16) | 0.98 |
| Female sex | 600 (73) | 1792 (73) | 0.96 |
| White, non-Hispanic | 804 (98) | 2393 (98) | 0.46 |
| Body mass index, kg/m2 (mean ±SD) | 30 (±7) | 29 (±7) | 0.03 |
| Received bachelor’s degree or higher | 324 (40) | 1182 (48) | < 0.001 |
| Ever smoker | 402 (49) | 993 (40) | < 0.001 |
IQR = interquartile range, kg = kilograms, m = meters, RA = rheumatoid arthritis, SD = standard deviation
Family history of comorbidities and RA risk
Self-reported family history of RA in a first-degree relative was associated with personal history of RA (adjusted OR [aOR] 2.44, 95% CI 2.02 to 2.94), as were family history of other rheumatologic autoimmune diseases (aOR 1.89, 95% CI 1.41 to 2.52), OA (aOR 1.41, 95% CI 1.19 to 1.68), and hyper/hypothyroidism (aOR 1.34, 95% CI 1.1 to 1.63). An interaction between family history of RA and educational level for RA risk existed such that the increase in RA risk associated with a family history of RA was smaller among those receiving a Bachelor’s degree or higher than among those without a Bachelor’s degree (aOR 2.37 versus 2.58, p<0.001 for interaction).
Family history of several other comorbidities were statistically associated with developing RA, but not at the pre-specified threshold of p<0.01. These included pulmonary fibrosis, IBD, fibromyalgia, OSA, and migraine headaches (Table 2). Similarly, two conditions were associated with a suggestive decrease in the risk of RA (p<0.05 but >0.01), including family history of type 2 diabetes (aOR 0.81, 95% CI 0.67 to 0.97) and Parkinson’s disease (aOR 0.70, 95% CI 0.49 to 0.98). None of the 22 cancer family history items were associated with developing RA, even when combined together (aOR 0.97, 95% CI 0.81 to 1.16). Table S1 shows the frequency of all of the family history items, including those that were not associated with RA.
Table 2.
Association between self-reported family history of comorbidities and developing RA, ordered by strength of association
| N (%) | ||||
|---|---|---|---|---|
| Comorbidity | RA Cases (N = 821) | Controls (N = 2455) | Unadjusted OR for RA (95% CI) | Adjusted OR for RA (95% CI)* |
| Rheumatoid arthritis | 272 (36) | 379 (18) | 2.56 (2.12,3.08) | 2.44 (2.02,2.94) |
| HIV | 9 (1) | 11 (0.5) | 2.47 (0.99,5.98) | - |
| Pulmonary fibrosis | 20 (3) | 29 (1) | 2.10 (1.17,3.72) | 2.12 (1.16,3.80) |
| Rheum. autoimmune dz.** | 82 (11) | 143 (7) | 1.84 (1.38,2.44) | 1.89 (1.41,2.52) |
| Autism | 24 (3) | 44 (2) | 1.66 (0.99,2.72) | 1.62 (0.96,2.67) |
| Tuberculosis | 34 (5) | 68 (3) | 1.52 (0.99,2.30) | 1.51 (0.98,2.30) |
| Inflammatory bowel disease | 62 (8) | 129 (6) | 1.49 (1.08,2.03) | 1.45 (1.05,1.98) |
| Celiac disease | 29 (4) | 62 (3) | 1.44 (0.91,2.24) | 1.45 (0.91,2.27) |
| Osteoarthritis | 403 (55) | 1075 (47) | 1.36 (1.15,1.61) | 1.41 (1.19,1.68) |
| Fibromyalgia | 74 (10) | 168 (8) | 1.39 (1.04,1.84) | 1.40 (1.04,1.86) |
| Hyper/hypothyroidism | 206 (28) | 490 (23) | 1.34 (1.11,1.62) | 1.34 (1.10,1.63) |
| Other liver disease | 61 (8) | 137 (6) | 1.36 (0.99,1.85) | 1.29 (0.93,1.77) |
| Obstructive sleep apnea | 205 (28) | 503 (22) | 1.32 (1.09,1.59) | 1.28 (1.05,1.55) |
| Other mental illness | 93 (13) | 222 (10) | 1.29 (0.99,1.66) | 1.24 (0.95,1.61) |
| Migraine headaches | 267 (36) | 703 (32) | 1.22 (1.02,1.45) | 1.20 (1.00,1.43) |
| Asthma | 232 (31) | 607 (27) | 1.20 (1.00,1.44) | 1.20 (0.99,1.44) |
| COPD | 125 (17) | 304 (14) | 1.27 (1.01,1.59) | 1.19 (0.94,1.50) |
| Venous thromboembolism | 125 (17) | 319 (15) | 1.20 (0.95,1.50) | 1.18 (0.94,1.48) |
| Type 2 diabetes | 214 (29) | 723 (32) | 0.85 (0.70,1.01) | 0.81 (0.67,0.97) |
| Parkinson’s disease | 44 (6) | 182 (8) | 0.71 (0.50,0.99) | 0.70 (0.49,0.98) |
CI = confidence interval, COPD = chronic obstructive pulmonary disease, dz = disease, HIV = human immunodeficiency virus, OR = odds ratio, RA = rheumatoid arthritis, rheum. = rheumatologic
Adjusting for age, sex, race, BMI, education, smoking (ever vs never). Bold values are statistically significant at p<0.01.
Prompted participants with the examples of scleroderma, lupus
Incident RA Subset
Among the incident RA subset (N=143) and their controls (N=426), family history of several comorbidities were associated with developing incident RA, including autism, celiac disease, other liver disease, OA, and hyper/hypothyroidism (Table 3). The association between self-reported family history of RA and personal history of incident RA was statistically significant, but not to the pre-specified p<0.01 threshold (OR 1.64, 95% CI 1.01 to 2.62).
Table 3.
Subgroup analyses in the 143 cases that developed RA after the questionnaire (incident RA) and their controls
| N (%) | |||
|---|---|---|---|
| Comorbidity | Incident RA (N = 143) | Controls (N = 426) | Unadjusted OR for Incident RA (95% CI)* |
| Rheumatoid arthritis | 33 (26) | 66 (17) | 1.64 (1.01,2.62) |
| HIV | 0 (0) | 4 (1) | - |
| Pulmonary fibrosis | 3 (2) | 5 (1) | 1.74 (0.35,7.21) |
| Rheum. autoimmune dz. | 9 (7) | 18 (4) | 1.56 (0.65,3.47) |
| Autism | 7 (5) | 2 (0.5) | 10.5 (2.51,71.3) |
| Tuberculosis | 5 (4) | 9 (2) | 1.68 (0.51,4.95) |
| Inflammatory bowel disease | 10 (8) | 19 (5) | 1.64 (0.71,3.55) |
| Celiac disease | 10 (8) | 7 (2) | 4.61 (1.73,12.9) |
| Osteoarthritis | 84 (61) | 157 (40) | 2.57 (1.70,3.92) |
| Fibromyalgia | 10 (7) | 29 (7) | 1.03 (0.46,2.10) |
| Hyper/hypothyroidism | 43 (33) | 62 (16) | 2.54 (1.61,4.00) |
| Other liver disease | 16 (12) | 15 (4) | 3.39 (1.62,7.13) |
| Obstructive sleep apnea | 39 (30) | 83 (21) | 1.55 (0.99,2.41) |
| Other mental illness | 17 (13) | 24 (6) | 2.18 (1.12,4.17) |
| Migraine headaches | 45 (34) | 123 (31) | 1.14 (0.74,1.72) |
| Asthma | 43 (32) | 100 (26) | 1.39 (0.90,2.12) |
| COPD | 18 (14) | 51 (13) | 1.03 (0.57,1.81) |
| Venous thromboembolism | 28 (21) | 46 (12) | 1.94 (1.14,3.23) |
| Type 2 diabetes | 42 (32) | 111 (28) | 1.20 (0.78,1.83) |
| Parkinson’s disease | 13 (10) | 25 (7) | 1.58 (0.76,3.14) |
CI = confidence interval, COPD = chronic obstructive pulmonary disease, dz = disease, HIV = human immunodeficiency virus, OR = odds ratio, RA = rheumatoid arthritis, rheum. = rheumatologic
Bold values are statistically significant at p<0.01.
Sensitivity Analyses
None of the family history items were more associated with one of the serological subtypes compared to the other (Table S2). Another sensitivity analysis examining the results with “don’t know” responses coded as “missing” showed similar results to the primary analysis (Table S3). We performed an additional sensitivity analysis with patients reporting type 2 diabetes as the comparison group to lessen the impact of illness-related recall bias. This showed similar associations for RA, other rheumatologic autoimmune diseases, hyper/hypothyroidism, IBD, and migraines, though the associations with pulmonary fibrosis, fibromyalgia, and OSA were weaker (Table S4). Finally, missing data analysis revealed that participants missing five or more family history items tended to be slightly older and less educated than those missing four or fewer (Table S5).
DISCUSSION
Our study found that family history of several diseases besides RA are associated with increased risk of RA, including other rheumatologic autoimmune diseases, autoimmune diseases like thyroid disease and IBD, and non-autoimmune conditions like pulmonary fibrosis, OSA, and autism. Furthermore, family history of some diseases were possibly associated with modestly decreased risk of RA, including Parkinson’s disease and type 2 diabetes. These findings highlight opportunities for better predicting risk of RA as well as understanding disease pathogenesis.
Other than family history of RA, the family history item most strongly associated with personal history of RA was other rheumatologic autoimmune diseases. A previous study showed that family history of lupus and connective tissue disease almost doubled odds of RA (20). Another found standardized incidence ratios of around two for parents of RA cases with localized scleroderma (2.4), lupus (2.1), and systemic sclerosis (1.7), along with significant associations with many other diseases including ankylosing spondylitis, Sjögren’s syndrome, sarcoidosis, psoriasis, and granulomatosis with polyangiitis. However, that study only used hospital diagnosis codes, which is likely to accurately identify severe disease but may not capture mild disease (2). Moreover, neither study controlled for confounders. A third study did control for sex and duration of RA and found the association with other rheumatologic diseases not to be statistically significant (OR 1.5, 95% CI 0.6 to 3.6) (33). The present study thus fills an important knowledge gap by finding an association between family history of other autoimmune diseases and RA even after controlling for several important confounders.
Family history of several non-rheumatologic autoimmune diseases were also associated with later developing RA. In the full analysis, hyper/hypothyroidism and IBD were associated with RA, while in the incident RA subset, hyper/hypothyroidism and celiac disease were associated with RA. Family history of thyroid disease has been previously shown to correlate with developing RA on an unadjusted basis (2, 34), while another adjusting for sex and RA duration found a nonsignificant association (OR 2.0, 95% CI 0.7 to 5.8). This study reconciles these inconsistencies by finding a smaller but significant association between autoimmune thyroid disease and RA after controlling for several important confounders. In contrast, the association between family history of IBD and celiac disease with RA found in this study is novel. These findings are likely to be valid since IBD and celiac disease are observed with greater frequency among patients with RA (26, 35, 36). The association between autoimmune diseases and RA also has a plausible mechanism, as shown by several studies showing overlapping genetic susceptibility (37, 38).
Family history of several non-autoimmune comorbidities were also associated with RA. Most notable among these were family history of pulmonary fibrosis and OSA, which trended toward significance with p<0.05 in the full analysis. These were not associated in the incident subset, though OSA trended toward significance. Family history of OA was also significant, perhaps due to misclassification of OA as RA since many people confuse the two conditions for each other. Family history of cancer was not associated with RA as originally hypothesized. No prior studies addressed the association between family history of pulmonary fibrosis or OSA and RA, though these diseases are associated with RA among RA cases themselves (26, 39–41). It is possible these associations occurred due to chance, as they were not significant to the p<0.01 threshold which was set a priori for multiple comparisons. Alternatively, perhaps there is a shared heritable component and/or environmental exposure for these diseases and RA.
Interestingly, family history of two comorbidities—type 2 diabetes and Parkinson’s disease—trended toward a decreased risk of RA. The negative association with type 2 diabetes is surprising given the known, positive association between type 2 diabetes and RA among people with RA (42–44). The negative association with Parkinson’s disease is new and intriguing yet is in contrast to a few prior studies showing a slightly increased risk of Parkinson’s disease (45) and dementia (46) among people with autoimmune rheumatic diseases. These findings were not significant to the pre-specified p<0.01 threshold, so may have occurred due to chance.
As found by previous studies, family history of RA was most strongly associated with RA. The strength of association in this study was similar to previous studies, which found odds ratios around three (1–4, 7, 10). The self-reported prevalence of RA family history in the current report (36%) was similar to a US Nurses’ Health Study report that also used self-reported family history (11) but higher than prior studies which were conducted in Scandinavia using register data (11, 20). This discrepancy might reflect misclassification from self-report, supported by the reduced RA family history reported in the incident subset. It might also reflect the low sensitivity of electronic diagnosis code methods, population differences, or that 8% of participants in this study did not answer the question.
Among the subset of RA cases that developed RA after completing the baseline questionnaire, or “incident RA” cases, the association with RA was also weaker than that of the full analysis. One reason may be that the incident RA cases, and even their family members, might have less RA-associated recall bias compared to individuals who had RA at the time of the questionnaire. Alternatively, before RA diagnosis, many people may not be familiar with autoimmune diseases such as RA, or with the distinction between RA and OA. Thus, the RA association may have been artificially lowered, and the OA association artificially elevated, due to misclassification of RA as OA and vice versa. Another reason for the differences between the full and incident RA analyses might be that they represent different forms of RA. The incident RA cases were diagnosed much later in life, and there may be different mechanisms that influence its development at later ages, with the genetic component relatively less important (47).
Several findings from the incident RA subset deserve mention. Like the full analysis, family history of several autoimmune diseases including celiac disease and hyper/hypothyroidism were associated with RA, as was the self-reported family history of OA. Unlike the full analysis, however, family history of autism and other liver disease were also highly associated with RA. No existing literature describes an association between family history of autism or liver diseases and RA. However, a meta-analysis showed that family history of autoimmune diseases including RA was associated with increased risk of autism (aOR 1.51) (48). Further, a recent Danish cohort study showed an association between parental RA and autism in offspring (HR 1.3) (49). This association between autism and RA may merit further study.
Strengths of this study include its broad range of comorbidities studied and adjustment for many key confounders. There are also several important limitations. First, using a convenience sample for selection creates potential selection bias which may limit generalizability, though this was somewhat mitigated by recruitment in primary care divisions and at multiple locations. Second, recall bias is possible when asking patients about RA and family history, especially family history of RA, as noted by the higher odds ratio in the full cohort compared to the incident subset. However, participants were asked about all comorbidities, not just RA. Furthermore, the other findings were overall similar in the incident RA subset and in the sensitivity analysis using participants with type 2 diabetes as controls. Third, the questionnaire asked participants whether they had family history of other rheumatologic autoimmune diseases as a whole, rather than specific diseases such as lupus or scleroderma, precluding any interpretation about family history of these diseases individually. Fourth, the percent of participants who did not respond for each family history item was somewhat high (7 to 13%), perhaps from an assumption that nonresponse meant absence of family history, and thus artificially inflating family history counts for each comorbidity. Nevertheless, sensitivity analysis coding these responses as missing did not change the results. While we did adjust for many possible confounders, residual or unmeasured confounding is still possible. Sample size was also too limited for the sensitivity analysis of incident RA to permit adjustment. Finally, using self-report rather than verified RA and family history likely results in misclassification, and to a different degree for each comorbidity. Future studies validating these findings using diagnosis codes would be helpful.
In summary, self-reported family history of several comorbidities besides RA were associated with increased risk for RA, including other rheumatologic diseases such as lupus and scleroderma, autoimmune diseases such as thyroid disease and IBD, and potentially non-autoimmune conditions such as pulmonary fibrosis, OSA, and autism. These findings can help refine tools to predict RA risk. Future studies exploring the mechanisms for such associations may help uncover RA disease pathogenesis.
Supplementary Material
SIGNIFICANCE AND INNOVATIONS.
After adjusting for potential confounders, family history of rheumatologic autoimmune diseases were associated with twice the odds of developing RA.
Family history of other autoimmune conditions such as thyroid disease and inflammatory bowel disease were also associated with elevated risk of RA.
These findings can help identify novel populations at risk for RA, improve RA risk prediction tools, and uncover RA pathogenesis.
Funding:
Dr. Kronzer is supported by the Rheumatology Research Foundation Resident Research Preceptorship. Dr. Sparks is supported by the National Institute for Arthritis and Musculoskeletal Skin Diseases (K23 AR069688, L30 AR066953, R03 AR075886, P30 AR070253, and P30 AR072577) and the Rheumatology Research Foundation K Supplement Award.
Footnotes
Disclosures: The authors have no potential conflicts of interest related to this work.
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