Citations

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.

PURPOSE Pretreatment estimates of seminal vesicle invasion (SVI) are challenging and significantly influence the management of prostate cancer. We sought to improve current models to predict SVI through the development of an SVI prediction genomic signature. PATIENTS AND METHODS A total of 15,889 patients who underwent radical prostatectomy (RP) with available baseline clinical, pathology, and transcriptome data were retrieved from the GRID registry (ClinicalTrials.gov identifier: NCT02609269 ) and other retrospective cohorts. These data were divided into a training (n = 6,766), test (n = 3,363), and two validation (n = 5,062 and 698) cohorts. Multivariable logistic regression was performed to assess the predictive effect of the genomic SVI (gSVI) classifier in the presence of established nomograms (Partin Tables and Memorial Sloan Kettering Cancer Center [MSKCC]). RESULTS In the training cohort, univariable filtering identified 2,132 genes that were differentially expressed between RP tumors with and without SVI. Model parameters were tuned to maximize the area under the curve (AUC) in the testing cohort, resulting in a logistic generalized linear model with 581 genes. The gSVI model scores range from 0 to 1. In the first validation set, gSVI showed superior discrimination of patients with and without SVI at RP compared with other prognostic signatures trained to predict distant metastasis or clinical recurrence. Of the 698 patients in the second validation set, gSVI combined with the MSKCC nomogram had a superior AUC (0.86) compared with either nomogram individually (0.81). CONCLUSION The gSVI represents a novel and validated expression signature to predict the presence of SVI before treatment with surgery. This genomic tool adds discriminatory power to existing clinical predictive nomograms and may help with pretreatment counseling and decision making.

Abstract Rationale Usual interstitial pneumonia (UIP) is the defining morphology of idiopathic pulmonary fibrosis (IPF). Guidelines for IPF diagnosis conditionally recommend surgical lung biopsy for histopathology diagnosis of UIP when radiology and clinical context are not definitive. A “molecular diagnosis of UIP” in transbronchial lung biopsy, the Envisia Genomic Classifier, accurately predicted histopathologic UIP. Objectives We evaluated the combined accuracy of the Envisia Genomic Classifier and local radiology in the detection of UIP pattern. Methods Ninety-six patients who had diagnostic lung pathology as well as a transbronchial lung biopsy for molecular testing with Envisia Genomic Classifier were included in this analysis. The classifier results were scored against reference pathology. UIP identified on high-resolution computed tomography (HRCT) as documented by features in local radiologists’ reports was compared with histopathology. Measurements and Main Results In 96 patients, the Envisia Classifier achieved a specificity of 92.1% (confidence interval [CI],78.6–98.3%) and a sensitivity of 60.3% (CI, 46.6–73.0%) for histology-proven UIP pattern. Local radiologists identified UIP in 18 of 53 patients with UIP histopathology, with a sensitivity of 34.0% (CI, 21.5–48.3%) and a specificity of 96.9% (CI, 83.8–100%). In conjunction with HRCT patterns of UIP, the Envisia Classifier results identified 24 additional patients with UIP (sensitivity 79.2%; specificity 90.6%). Conclusions In 96 patients with suspected interstitial lung disease, the Envisia Genomic Classifier identified UIP regardless of HRCT pattern. These results suggest that recognition of a UIP pattern by the Envisia Genomic Classifier combined with HRCT and clinical factors in a multidisciplinary discussion may assist clinicians in making an interstitial lung disease (especially IPF) diagnosis without the need for a surgical lung biopsy.

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.