Davis, John M.

AbstractWomen have up to a fourfold increase in risk for autoimmune disease compared to men. Many explanations have been proposed, including sex hormones, the X chromosome, microchimerism, environmental factors, and the microbiome. However, the mechanism for this autoimmune sex bias remains obscure. In this manuscript, we evaluate the hypothesis that qualitative or quantitative differences in circulating antibodies may explain, at least in part, the pathogenesis of autoimmune disease and its sex bias—especially when considering an evolutionary perspective. Indeed, women have higher absolute levels of antibodies than men, and (auto)antibodies are also associated with most autoimmune diseases. Several facts suggest differences in antibodies may cause increased prevalence of autoimmune disease in women. First, the association between increased quantities of serum antibodies and increased prevalence of autoimmunity is found not only in women, but also in men with Klinefelter syndrome. Second, both serum antibody levels and autoimmunity spike in the postpartum period. Third, a dose–response effect exists between parity and both serum antibodies and prevalence of autoimmune disease. Fourth, many biologically plausible mechanisms explain the association, such as T cell‐dependent activation of B cells and/or VGLL3. The evolutionary underpinning of increased antibodies in women is likely to be protection of offspring from infections. Overall, this evolutionary paradigm can help explain why the phenomenon of autoimmunity occurs preferentially in women and raises the possibility of new treatment options.

Abstract Objective The study sought to determine the dependence of the Electronic Medical Records and Genomics (eMERGE) rheumatoid arthritis (RA) algorithm on both RA and electronic health record (EHR) duration. Materials and Methods Using a population-based cohort from the Mayo Clinic Biobank, we identified 497 patients with at least 1 RA diagnosis code. RA case status was manually determined using validated criteria for RA. RA duration was defined as time from first RA code to the index date of biobank enrollment. To simulate EHR duration, various years of EHR lookback were applied, starting at the index date and going backward. Model performance was determined by sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC). Results The eMERGE algorithm performed well in this cohort, with overall sensitivity 53%, specificity 99%, positive predictive value 97%, negative predictive value 74%, and AUC 76%. Among patients with RA duration <2 years, sensitivity and AUC were only 9% and 54%, respectively, but increased to 71% and 85% among patients with RA duration >10 years. Longer EHR lookback also improved model performance up to a threshold of 10 years, in which sensitivity reached 52% and AUC 75%. However, optimal EHR lookback varied by RA duration; an EHR lookback of 3 years was best able to identify recently diagnosed RA cases. Conclusions eMERGE algorithm performance improves with longer RA duration as well as EHR duration up to 10 years, though shorter EHR lookback can improve identification of recently diagnosed RA cases.

Objective To investigate the effect of therapies on radiographic progression in patients with axial spondyloarthritis (SpA). Methods A comprehensive database search for studies assessing radiographic progression in axial SpA (particular treatment versus no treatment of interest) was performed. Study‐specific standardized mean differences in treatment outcomes at 2 and ≥4 years were estimated and combined using random‐effects models. Results Twenty‐four studies in patients with axial SpA were identified, of which 18 involved tumor necrosis factor inhibitors ( TNF i), 8 involved nonsteroidal antiinflammatory drugs ( NSAID s), and 1 involved secukinumab. Spinal radiographic progression, as measured by the modified Stoke Ankylosing Spondylitis Spine Score ( mSASSS ), was not significantly different between TNF i‐treated and biologics‐naive patients at 2 years ( mSASSS difference −0.73 [95% confidence interval (95% CI ) −1.52, 0.12], I 2 = 28%) and ≥4 years ( mSASSS difference −2.03 [95% CI −4.63, 0.72], I 2 = 63%). Sensitivity analyses restricted to studies with a low risk of bias showed a significant difference in spinal radiographic progression between TNF i‐treated and biologics‐naive patients at ≥4 years ( mSASSS difference −2.17 [95% CI −4.19, −0.15]). No significant difference in spinal radiographic progression was observed between NSAID ‐treated and control patients ( mSASSS difference −0.30 [95% CI −2.62, 1.31], I 2 = 71%) or between secukinumab‐treated and biologics‐naive patients ( mSASSS difference −0.34 [95% CI −0.85, 0.17]). With regard to treatment differences in patients with nonradiographic axial SpA or in patients with radiographic progression measured using the sacroiliac joint score, an insufficient number of studies were available for analysis. Conclusion Although no significant protective effect of TNF i treatment on spinal radiographic progression was seen over the course of 2 years or ≥4 years in patients with axial SpA, our analysis restricted to studies with a low risk of bias showed a protective effect of TNF i after ≥4 years. Therefore, long‐term TNF i exposure might confer beneficial effects on spinal radiographic progression in axial SpA. No difference in radiographic progression at 2 years was seen in either the NSAID or secukinumab treatment groups compared to their controls. Future studies should explore the effects of biologic treatment on radiographic progression, as well as the effects of long‐term biologics exposure, in patients with early axial SpA or those with nonradiographic axial SpA.

Objective Since comorbidities such as autoimmune diseases may be associated with rheumatoid arthritis (RA) risk, we hypothesized that a 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 3 controls to each case based on age, sex, recruitment year, and location. Patients self reported family history and characteristics (adjusted). Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs) for RA risk according to the presence of family history for each comorbidity, adjusted for body mass index, race, and smoking. Results Family history of several conditions was associated with developing RA, including rheumatic autoimmune diseases (OR adj 1.89 [95% CI 1.41–2.52]), pulmonary fibrosis (OR adj 2.12 [95% CI 1.16–3.80]), inflammatory bowel disease (OR adj 1.45 [95% CI 1.05–1.98]), hyper/hypothyroidism (OR adj 1.34 [95% CI 1.10–1.63]), and obstructive sleep apnea (OR adj 1.28 [95% CI 1.05–1.55]). Parkinson’s disease and type 2 diabetes mellitus were associated with a statistically decreased risk of RA that did not reach the prespecified significance threshold of P < 0.01 (OR adj 0.70 [95% CI 0.49–0.98] and OR adj 0.81 [95% CI 0.67–0.97], respectively). Analyses among 143 cases of incident RA were similar and also suggested an association with a family history of autism (OR 10.5 [95% CI 2.51–71.3]). Conclusion Family history of several autoimmune and nonautoimmune comorbidities was associated with increased risk of RA, providing an opportunity to identify novel populations at risk for RA.

ObjectiveThe objective of this study is to compare biologic drug discontinuation rates for older‐ versus younger‐onset rheumatoid arthritis (YORA) because this is a key outcome measure that could impact prescribing practices.MethodsWe performed a retrospective medical record review of all patients who fulfilled the 1987 American College of Rheumatology (ACR) criteria for adult‐onset rheumatoid arthritis (RA) in 1999‐2013 among residents of a geographically defined area, with follow‐up until death, migration, or July 1, 2017. Discontinuation rates were estimated using cumulative incidence adjusted for the competing risk of death.ResultsA total of 240 cases of elderly‐onset rheumatoid arthritis (EORA) and 366 cases of YORA were identified (65% and 73% female, respectively; P = 0.025). Cumulative incidence of biologic initiation was lower among the EORA cohort compared with the YORA cohort (18% vs 33%, respectively, at 10 years after RA incidence; P < 0.001). Among those treated with a biologic, years from RA diagnosis to first biologic treatment was not significantly different between the two groups (P = 0.62). Drug survival of first biologic was 64% at 1 year (95% confidence interval [CI]: 45%‐77%) and 53% at 2 years (95% CI: 33%‐66%) for EORA, compared with 61% at 1 year (95% CI: 50%‐69%) and 45% at 2 years (95% CI: 34%‐53%) for YORA (P = 0.75). Concurrent glucocorticoid use at initiation of first biologic was statistically and significantly associated with a lower risk of discontinuation in EORA (hazard ratio 0.21; 95% CI: 0.08‐0.53) but not in YORA (interaction P = 0.04).ConclusionDrug survival rates of biologic medications did not differ significantly between patients with EORA and YORA.

ObjectiveRheumatoid 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 at start of smoking.MethodsFor this case–control study, we identified 1,023 cases of RA (175 incident) within a single‐center biobank population, using a rules‐based algorithm that combined self‐report with 2 diagnostic codes. Exposures were self‐reported on biobank questionnaires. Logistic regression models were used to calculate the association of exposures with RA, adjusting for potential confounders. Odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated.ResultsAfter adjustment for allergies, urban environment, and passive smoke exposure, asthma was found to be associated with RA in the full cohort (OR 1.28 [95% CI 1.04–1.58; P = 0.02]) but not the incident RA cohort (OR 1.17 [95% CI 0.66–2.06; P = 0.60]). History of allergic disease was associated with RA in both the full cohort (OR 1.30 [95% CI 1.12–1.51; P < 0.001]) and the incident RA cohort (OR 1.61 [95% CI 1.11–2.33; P = 0.01]), especially food allergy, which was significantly associated with RA in the full cohort (OR 1.38 [95% CI 1.08–1.75; P = 0.01]) and showed a trend toward significance in the incident RA cohort (OR 1.83 [95% CI 0.97–3.45; P = 0.06]). Passive smoke exposure at home or work was not associated with RA. Finally, age at start of smoking was not associated with increased odds of developing RA in either the full cohort (OR 1.03 [95% CI 1.00–1.06; P = 0.03]) or the incident RA cohort (OR 1.00 [95% CI 0.92–1.08; P = 0.98]).ConclusionAsthma and allergies may be associated with increased risk of RA. Passive smoke exposure and early age at start of smoking do not appear to influence risk of RA.