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Background Sarcoidosis is an inflammatory, noncaseating, granulomatous disorder of unknown cause that can affect any body system and is associated with cardiovascular disease including sudden cardiac death (SCD). Cardiac involvement in sarcoidosis is associated with higher risk of SCD, but the SCD risk in the general sarcoidosis population is unknown. We aimed to determine the risk of SCD in people with sarcoidosis versus the matched general population. Methods and Results A population‐based cohort of sarcoidosis and age‐ and sex‐matched comparators from January 1, 1976 to December 31, 2013 was used; presence of other comorbidities in the comparator group was not an exclusion criterion. Mortality, including time, place, and cause of death were measured and manually adjudicated for SCD events. Incidence rates are reported per 100 000 person‐years, and Cox models were used for group comparisons. Of the 345 incident cases of sarcoidosis (171 men; 50%) there were 58 reported deaths; 10 were definite/probable SCD versus 57 all‐cause and 9 SCDs in comparators. Median follow‐up was 12.9 years (interquartile range, 6.0–23.4 years) . Incidence rate of SCD in sarcoidosis was 192 (95% CI, 92–352) versus 155 (95% CI, 71–294) in comparators (hazard ratio [HR], 1.28 (95% CI, 0.52–3.17). Nocturnal deaths were more frequent in sarcoidosis 57 (95% CI, 12–168) versus 17 (95% CI, 0.4–95) (HR, 3.76 [95% CI, 0.39–36.47]). No significant differences were detected between the groups by sex, age, calendar year of diagnosis, or disease duration. Conclusions In a population‐based cohort of patients with sarcoidosis, the risk for SCD compared with matched comparators was not increased. There were more nocturnal deaths among patients with sarcoidosis, yet this was statistically insignificant.

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.

ObjectiveThe study objective was to identify differences in gene expression between treatment responders (TRs) and treatment non‐responders (TNRs) diagnosed with juvenile dermatomyositis (JDM).MethodsGene expression analyses were performed using whole blood messenger RNA sequencing in patients with JDM (n = 17) and healthy controls (HCs; n = 10). Four analyses were performed (A1‐4) comparing differential gene expression and pathways analysis exploiting the timing of sample acquisition and the treatments received to perform these comparative analyses. Analyses were done at diagnosis and follow‐up, which averaged 7 months later in the cohort.ResultsAt diagnosis, the expression of 10 genes differed between TRs and TNRs. Hallmark and canonical pathway analysis revealed 11 and 60 pathways enriched in TRs and 3 and 21 pathways enriched in TNRs, respectively. Pathway enrichment at diagnosis in TRs was strongest in pathways involved in metabolism, complement activation, and cell signaling as mediated by IL‐8, p38/microtubule associated protein kinases (MAPK)/extracellular signal‐regulated kinases (ERK), Phosphatidylinositol 3 Kinase Gamma (PI3Kγ), and the B cell receptor. Follow‐up hallmark and canonical pathway analysis showed that 2 and 14 pathways were enriched in TRs, whereas 24 and 123 pathways were enriched in treatment TNRs, respectively. Prior treatment with glucocorticoids significantly altered expression of 13 genes in the analysis of subjects at diagnosis with JDM as compared with HCs.ConclusionNumerous genes and pathways differ between TRs and TNRs at diagnosis and follow‐up. Prior treatment with glucocorticoids prior to specimen acquisition had a small effect on the performed analyses.

The COVID-19 pandemic has become an unprecedented facilitator of rapid telehealth expansion within rheumatology. Due to demographic shifts and workforce shortages in the future, new models of rheumatology care will be expected to emerge, with a growing footprint of telehealth interventions. Telehealth is already being used to monitor patients with rheumatic diseases and initial studies show good results in terms of safety and disease progression. It is being used as a tool for appointment prioritization and triage, and there is good evidence for using telehealth in rehabilitation, patient education and self-management interventions. Electronic patient-reported outcomes (ePROs) offer a number of long-term benefits and opportunities, and a routine collection of ePROs also facilitates epidemiological research that can inform future healthcare delivery. Telehealth solutions should be developed in close collaboration with all stakeholders, and the option of a telehealth visit must not deprive patients of the possibility to make use of a conventional ‘face-to-face’ visit. Future studies should especially focus on optimal models for rheumatology healthcare delivery to patients living in remote areas who are unable to use or access computer technology, and other patient groups at risk for disparity due to technical inequity and lack of knowledge.

Objective To determine the association of allergic conditions with incident rheumatoid arthritis (RA), especially in relation to smoking history and anti-citrullinated peptide antibody (ACPA) status. Methods This case–control study included 3515 incident RA cases and 5429 matched controls from the Epidemiological Investigation of Rheumatoid Arthritis study 1995 to 2016, including questionnaire-based information on eight allergic conditions composed from a list of 59 unique allergies. We used logistic regression and adjusted ORs (aOR) to assess the association between allergic conditions and risk of RA, adjusting for age, sex, residential area, body mass index, education, and smoking, and stratified by smoking and ACPA. Results A history of any reported allergy was equally common in RA (n=1047, 30%) as among population controls (n=1540, 29%), aOR 1.04, 95% CI 0.95 to 1.15. Metal, respiratory, food, plant/pollen and chemical allergies were not associated with risk of RA. By contrast, statistically significant associations were observed for animal dander allergy (6% vs 5%, aOR 1.37, 95% CI 1.03 to 1.82), especially in ACPA-positive RA (aOR 1.46 95% CI 1.06 to 2.01) and for atopic dermatitis, in particular for older and ACPA-negative RA (aOR 2.33, 95% CI 1.37 to 3.96 at age 80). Never smokers with allergic rhinitis also had increased risk of developing RA (aOR 1.30, 95% CI 1.00 to 1.68). Conclusion Most common allergies do not increase risk of RA, nor do they protect against RA. However, some allergic conditions, notably animal dander allergy, atopic dermatitis and allergic rhinitis, were associated with an increased risk for RA.

Objective To comprehensively assess multimorbidity burden in patients with rheumatoid arthritis (RA) in order to unify the multimorbidity definition for RA research and clinical practice. Methods In this population-based study, residents of eight Minnesota counties with prevalent RA on 1 January 2015 were identified. Age, sex and county-matched non-RA comparators were selected from the same population. Diagnostic codes were retrieved for 5 years before 1 January 2015. Using two codes ≥30 days apart, 44 previously defined morbidities and 78 non-overlapping chronic disease categories based on Clinical Classification Software were defined. Prevalence of each morbidity in the RA versus non-RA cohorts was compared using false discovery rate to adjust for multiple comparisons. Morbidities more common in RA than non-RA and those with prevalence ≥5% were retained. Results 1643 patients with RA and 1643 non-RA subjects (72% women; mean age 63.1 years) were studied. Using the 44 morbidities, multimorbidity (defined as 2+ morbidities) was present in 1411 (86%) of RA and 1164 (71%) of non-RA subjects (p<0.001) with 5+ morbidities present in 907 (55%) of RA and 619 (38%) of non-RA (p<0.001). Patients with RA had significantly higher prevalence of 24 of the 44 morbidities compared with non-RA, especially interstitial lung disease, fibromyalgia, osteoarthritis and osteoporosis. Among the additional 78 categories, 7 were significantly higher in RA than non-RA, including organic sleep disorders, vitamin D deficiency and foot ulcers. Conclusion Patients with RA have a higher prevalence of multimorbidity compared with non-RA subjects. These results confirm the list of 44 morbidities and add several other morbidities of interest in RA.

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.