Davis, John M

Although the etiology of rheumatoid arthritis (RA) is unknown, a strong genetic predisposition and the presence of preclinical antibodies before the onset of symptoms is documented. An expansion of Eggerthella lenta is associated with severe disease in RA. Here, using a humanized mouse model of collagen-induced arthritis, we determined the impact of E. lenta abundance on RA severity. Naïve mice gavaged with E. lenta produce preclinical rheumatoid factor and, when induced for arthritis, develop severe disease. The augmented antibody response was much higher in female mice, and among patients with RA, women had higher average load of E. lenta . Expansion of E. lenta increased CXCL5 and CD4 T cells, and both interleukin-17– and interferon-γ–producing B cells. Further, E. lenta gavage caused gut dysbiosis and decline in amino acids and nicotinamide adenine dinucleotide with an increase in microbe-dependent bile acids and succinyl carnitine causing systemic senescent-like inflammation.

ObjectiveTo identify differences in multimorbidity and individual comorbidities among individuals with rheumatoid arthritis (RA), separated by race and ethnicity.MethodsThis case–control study within OptumLabs Data Warehouse from 2010 to 2019 matched RA cases (defined by 2 codes plus prescription of an RA drug) to non‐RA controls 1:1 on age, sex, race and ethnicity, region, index date of RA, and insurance coverage duration. We defined multimorbidity as the presence of ≥2 or ≥5 validated comorbidities. Logistic regression models calculated adjusted odds of multimorbidity with 95% confidence intervals (95% CIs) within each race and ethnicity.ResultsWe identified 154,391 RA cases and 154,391 controls (mean age 59.6, 76% female). Black enrollees had the most multimorbidity ≥2/≥5 (73.1%, 34.3%); Asian enrollees had the least (52.4%, 17.3%). Adjusted odds of multimorbidity ≥2 and ≥5 in RA cases versus controls was 2.19 (95% CI 2.16–2.23) and 2.06 (95% CI 2.02–2.09), respectively. This increase was similar across race and ethnicity. However, we observed elevated occurrence of certain comorbidities by race and ethnicity versus controls (P < 0.001), including renal disease in White enrollees (4.7% versus 3.2%) and valvular heart disease in Black and White enrollees (3.2% and 2.8% versus 2.6% and 2.2%).ConclusionMultimorbidity is a problem for all RA patients. Targeted identification of certain comorbidities by race and ethnicity may be a helpful approach to mitigate multimorbidity.

Objective To identify clusters of comorbidities in patients with rheumatoid arthritis (RA) using 4 methods and to compare to patients without RA. Methods In this retrospective, population‐based study, residents of 8 Minnesota counties with prevalent RA as of January 1, 2015 were identified. Age‐, sex‐, and county‐matched non‐RA comparators were selected from the same underlying population. Diagnostic codes were retrieved for 5 years before January 1, 2015. Using 2 codes ≥30 days apart, 44 previously defined morbidities and 11 nonoverlapping chronic disease categories based on Clinical Classifications Software were defined. Unsupervised machine learning methods of interest included hierarchical clustering, factor analysis, K‐means clustering, and network analysis. Results Two groups of 1,643 patients with and without RA (72% female; mean age 63.1 years in both groups) were studied. Clustering of comorbidities revealed strong associations among mental/behavioral comorbidities and among cardiovascular risk factors and diseases. The clusters were associated with age and sex. Differences between the 4 clustering methods were driven by comorbidities that are rare and those that were weakly associated with other comorbidities. Common comorbidities tended to group together consistently across approaches. The instability of clusters when using different random seeds or bootstrap sampling impugns the usefulness and reliability of these methods. Clusters of common comorbidities between RA and non‐RA cohorts were similar. Conclusion Despite the higher comorbidity burden in patients with RA compared to the general population, clustering comorbidities did not identify substantial differences in comorbidity patterns between the RA and non‐RA cohorts. The instability of clustering methods suggests caution when interpreting clustering using 1 method.

ObjectiveTo identify the incidence, risk factors, and outcomes of rheumatoid arthritis–associated interstitial lung disease (RA‐ILD) and to assess time trends in the incidence and mortality in RA‐ILD.MethodsWe included adult residents of Olmsted County, Minnesota with incident RA between 1999 and 2014. Subjects were followed until death, emigration, or April 30, 2019. ILD was defined as the presence of a radiologist‐defined pattern consistent with ILD on chest computed tomography (CT). When chest CT was absent, the combination of chest radiograph abnormalities compatible with ILD and restrictive pattern on pulmonary function testing was considered consistent with ILD. Potential risk factors included age, sex, smoking, obesity, seropositivity, extraarticular manifestations (EAMs), and medications. For survival analysis, we matched RA‐ILD patients to RA–non‐ILD comparators. The frequency and mortality from clinician‐diagnosed RA‐ILD from 1999 to 2014 was compared against a cohort from 1955 to 1994.ResultsDuring the 1999–2014 time period, 645 individuals (70% women) had incident RA, were a median age of 55.3 years, and 53% never smoked. Twenty‐two patients had ILD before RA, and 51 (67% women) developed ILD during follow‐up. The 20‐year cumulative incidence of RA‐ILD was 15.3%. Ever‐smoking (hazard ratio [HR] 1.92), age at RA onset (HR 1.89 per 10‐year increase), and severe EAMs (HR 2.29) were associated with incident RA‐ILD. The RA‐ILD cases had higher mortality than their matched RA comparators (HR 2.42). Incidence of RA‐ILD was non‐significantly lower from 1999 to 2014 than from 1955 to1994, but mortality was improved.ConclusionsRA‐ILD occurs in nearly 1 in 6 patients with RA within 20 years and is associated with shorter survival. Lack of significant change in RA‐ILD incidence over 6 decades deserves further investigation.

Objective To test the ability of machine learning (ML) approaches with clinical and genomic biomarkers to predict methotrexate treatment response in patients with early rheumatoid arthritis (RA). Methods Demographic, clinical, and genomic data from 643 patients of European ancestry with early RA (mean age 54 years; 70% female) subdivided into a training (n = 336) and validation cohort (n = 307) were used. The genomic data comprised 160 single‐nucleotide polymorphisms (SNPs) previously associated with RA or methotrexate metabolism. Response to methotrexate monotherapy was defined as good or moderate by the European Alliance of Associations for Rheumatology (EULAR) response criteria at the 3‐month follow‐up. Supervised ML methods were trained with 5 repeats and 10‐fold cross‐validation using the training cohort. Prediction performance was validated in the independent validation cohort. Results Supervised ML methods combining age, sex, smoking, rheumatoid factor, baseline Disease Activity Score in 28 joints (DAS28) scores and 160 SNPs predicted EULAR response at 3 months with the area under the receiver operating curve of 0.84 ( P = 0.05) in the training cohort and achieved a prediction accuracy of 76% ( P = 0.05) in the validation cohort (sensitivity 72%, specificity 77%). Intergenic SNPs rs12446816, rs13385025, rs113798271, and ATIC (rs2372536) had variable importance above 60.0 and along with baseline DAS28 scores were among the top predictors of methotrexate response. Conclusion Pharmacogenomic biomarkers combined with baseline DAS28 scores can be useful in predicting response to methotrexate in patients with early RA. Applying ML to predict treatment response holds promise for guiding effective RA treatment choices, including timely escalation of RA therapies.

ObjectiveWe aimed to determine the population‐based incidence, prevalence, and mortality of dermatomyositis (DM) using European Alliance of Associations for Rheumatology (EULAR)/American College of Rheumatology (ACR) criteria.MethodsThis population‐based cohort study included incident DM from January 1, 1995 to December 31, 2019. We manually reviewed all individuals with at least 1 code for DM or polymyositis to determine if they met EULAR/ACR criteria, subspecialty physician diagnosis, and/or Bohan and Peter criteria. We age‐ and sex‐adjusted incidence and prevalence estimates to the US non‐Hispanic White year 2000 population and estimated prevalence on January 1, 2015. Standardized mortality ratios (SMRs) with 95% confidence intervals (95% CIs) compared observed to expected mortality adjusting for age, sex, and year.ResultsWe identified 40 cases of verified DM, with 29 cases incident in Olmsted County from 1995 to 2019. The mean age was 57 years, 26 (90%) were female, and 12 (41%) had clinically amyopathic DM (CADM). The median follow‐up time was 8.2 years. The overall adjusted incidence of DM was 1.1 (95% CI 0.7–1.5) per 100,000 person‐years, and prevalence was 13 (95% CI 6–19) per 100,000. The SMR was significantly elevated among the myopathic DM cases (3.1 [95% CI 1.1–6.8]) but not CADM cases (1.1 [95% CI 0.2–3.3]). The positive predictive value of ≥2 DM codes was only 40 of 82 (49%).ConclusionThis population‐based study found that DM incidence and prevalence were higher than previously reported. Mortality was significantly elevated for myopathic DM but not for CADM.

ObjectiveTreat‐to‐target (T2T) and shared decision‐making are valued features of current guidelines for rheumatoid arthritis (RA) management. Although T2T has demonstrated value for improving RA outcomes, implementation remains inconsistent and lacks standardization and procedures for including patient input. We sought to better understand the impact of shared decisions on T2T and how treatment goal discussions between patients and providers impact RA treatment improvement and satisfaction.MethodsAn anonymous, web‐based questionnaire was presented to United States residents aged 18 years or older with a self‐reported diagnosis of RA by a medical professional with 28 questions regarding socio‐demographics, RA disease activity (DA), diagnosis, treatments, outcomes, and goals. Analyses included descriptive statistics with χ2 and rank sum tests for comparisons.ResultsThe questionnaire was completed by 907 people (mean age of 58 years; mean 11 years since diagnosis; 90% female). The majority (571; 63%) did not discuss RA treatment goals with providers. Patients engaging in treatment goal discussions with their providers were three times more likely to be satisfied with their treatment plans. Patients discussing treatment goals with their providers were more likely to have improved DA levels and 68% more likely to reach remission.ConclusionA majority of patients with RA report having no treatment goal discussion with their providers; however, these discussions are associated with greater DA improvement and treatment satisfaction. Further research should seek understanding of how shared treatment goal discussions relate to successful RA management and explore the development of practical tools to implement them in regular clinic practice as part of a T2T regimen.

ObjectiveThe objective of this study was to describe differences in the clinical course of patients with rheumatoid arthritis (RA) who are antinuclear antibody (ANA)–positive compared with those who are ANA‐negative.MethodsThis was a retrospective population‐based cohort study of residents in Olmsted County, Minnesota, who first fulfilled 1987 American College of Rheumatology criteria for RA in 2009‐2014. Data were collected on first documentation of joint swelling. Data on rheumatoid factor or anti‐cyclic citrullinated peptide antibody testing and the ANA level were also collected. Comparisons between groups were performed by using χ2 and rank sum tests.ResultsIn this cohort, 64% of patients were tested for ANA within ±90 days of RA criteria fulfillment. In the161 patients with ANA testing, 25% were ANA‐positive. Patients who were ANA‐positive were younger, female, and less likely to be current smokers. ANA positivity did not differ between patients with RA who were seropositive and seronegative. In seropositive patients who were ANA‐positive, there was an increased time to fulfillment of RA criteria, increased time to treatment with disease‐modifying antirheumatic drugs (DMARDs), and increased likelihood of being treated with hydroxychloroquine as opposed to methotrexate. Other outcomes, including disease activity and mortality, did not differ significantly between groups.ConclusionIn patients with RA, important differences exist between those who are ANA‐positive and ANA‐negative in terms of time to fulfillment of RA criteria and time to DMARD initiation as well as choice of initial pharmacotherapy. These findings could indicate a difference in clinical presentation or perception of patients with RA who are ANA‐positive. Further research is needed to study the long‐term outcomes of patients with RA who are ANA‐positive.

ObjectiveTo determine the incidence of psoriatic arthritis (PsA) in a US population and describe trends in incidence and mortality over 5 decades.MethodsThe previously identified population‐based cohort that included Olmsted County, Minnesota residents ≥18 years of age who fulfilled PsA criteria during 1970–1999 was extended to include patients with incident PsA during 2000–2017. Age‐ and sex‐specific incidence rates and point prevalence, adjusted to the 2010 US White population, were reported.ResultsThere were 164 incident cases of PsA in 2000–2017 (mean ± SD age 46.4 ± 12.0 years; 47% female). The overall age‐ and sex‐adjusted annual incidence of PsA per 100,000 population was 8.5 (95% confidence interval [95% CI] 7.2–9.8) and was higher in men (9.3 [95% CI 7.4–11.3]) than women (7.7 [95% CI 5.9–9.4]) in 2000–2017. Overall incidence was highest in the 40–59 years age group. The incidence rate was relatively stable during 2000–2017, with no evidence of an overall increase or an increase in men only (but a modest increase of 3% per year in women), compared to 1970–1999 when a 4%‐per‐year increase in incidence was observed. Point prevalence was 181.8 per 100,000 population (95% CI 156.5–207.1) in 2015. The percentage of women among those with PsA increased from 39% in 1970–1999 and 41% in 2000–2009 to 54% in 2010–2017 (P = 0.08). Overall survival in PsA did not differ from the general population (standardized mortality ratio 0.85 [95% CI 0.61–1.15]).ConclusionThe incidence of PsA in this predominantly White US population was stable in 2000–2017, in contrast to previous years. However, an increasing proportion of women with PsA was found in this study.

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.