Molecular signature of neutrophil extracellular trap mediating disease module in idiopathic inflammatory myopathy.

The rarity and heterogeneity of idiopathic inflammatory myopathy (IIM) pose challenges for researching IIM in affected individuals. We analyzed integrated transcriptomic datasets obtained using muscle tissues from patients with five distinct IIM subtypes to investigate the shared and distinctive cellular and molecular characteristics. A transcriptomic dataset of muscle tissues from normal controls (n = 105) and patients with dermatomyositis (n = 89), polymyositis (n = 33), inclusion body myositis (n = 121), immune-mediated necrotizing myositis (n = 75), and anti-synthetase syndrome (n = 18) was used for differential gene-expression analysis, functional-enrichment analysis, gene set-enrichment analysis, disease-module identification, and kernel-based diffusion scoring. Damage-associated molecular pattern-associated pathways and neutrophil-mediated immunity were significantly enriched across different IIM subtypes, although their activities varied. Interferons-signaling pathways were differentially activated across all five IIM subtypes. In particular, neutrophil extracellular trap (NET) formation was significantly activated and correlated with Fcγ R-mediated signaling pathways. NET formation-associated genes were key for establishing disease modules, and FCGRs, C1QA, and SERPINE1 markedly perturbed the disease modules. Integrated transcriptomic analysis of muscle tissues identified NETs as key components of neutrophil-mediated immunity involved in the pathogenesis of IIM subtypes and, thus, has therapeutically targetable value.

Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting.

BACKGROUND: Predicting radiographic progression in axial spondyloarthritis (axSpA) remains limited because of the complex interaction between multiple associated factors and individual variability in real-world settings. Hence, we tested the feasibility of artificial neural network (ANN) models to predict radiographic progression in axSpA. METHODS: In total, 555 patients with axSpA were split into training and testing datasets at a 3:1 ratio. A generalized linear model (GLM) and ANN models were fitted based on the baseline clinical characteristics and treatment-dependent variables for the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS) of the radiographs at follow-up time points. The mSASSS prediction was evaluated, and explainable machine learning methods were used to provide insights into the model outcome or prediction. RESULTS: The R2 values of the fitted models were in the range of 0.90-0.95 and ANN with an input of mSASSS as the number of each score performed better (root mean squared error (RMSE) = 2.83) than GLM or input of mSASSS as a total score (RMSE = 2.99-3.57). The ANN also effectively captured complex interactions among variables and their contributions to the transition of mSASSS over time in the fitted models. Structural changes constituting the mSASSS scoring systems were the most important contributing factors, and no detectable structural abnormalities at baseline were the most significant factors suppressing mSASSS change. CONCLUSIONS: Clinical and radiographic data-driven ANN allows precise mSASSS prediction in real-world settings. Correct evaluation and prediction of spinal structural changes could be beneficial for monitoring patients with axSpA and developing a treatment plan.

De novo molecular subtyping of salivary gland tissue in the context of Sjögren's syndrome heterogeneity.

Understanding the mechanistic features and molecular taxonomy of diseases holds promise for the development of more effective treatments, especially for complex heterogeneous diseases. Here, we analyzed transcriptomic datasets of salivary gland tissues from patients with Sjögren's syndrome (SjS) to identify shared and divergent cellular and molecular signatures. Three molecular subtypes of SjS salivary gland tissue were identified: oxidative phosphorylation (OxPhos)-dominant (C1), weak inflammatory with type I interferon signatures (C2), and B cell receptor (BCR) signaling pathway-dominant (C3). C3 had the highest focus score. Type I helper T cells and B cells were the dominant cell types in C1 and C3 tissues, respectively. Metformin and drugs targeting PI3K, BTK, and JAKs were predicted to be effective treatments for C1 and C3 subtypes, respectively. Three subtypes of SjS salivary gland with distinct molecular signatures were identified. The results could contribute to optimal stratification of patients for more effective treatment approaches.

Marker-Assisted Backcrossing (MABc) to Improve Eating Quality with Thin Seed Coat and Aleurone Layer of Non-Glutinous Japonica Variety in Rice.

Brown rice is composed of rice bran, pericarp, seed coat, and aleurone layers, and the rice bran layer contains a large number of substances useful for the human body, such as dietary fiber, α-tocopherol, α-tocotrienol, and vitamins. However, more than 90% of these substances are removed when polished, and white rice has the disadvantage of losing food-related ingredients, such as umami-related amino acids, when compared to the unpolished group. In this study, we tried to develop new breeding lines with a thinner seed coat and aleurone layer to provide high eating quality with softer chewing characteristics and processability in rice grain. We detected an SNP for foreground selection for the backcross population by comparing genome sequences between Samgwang and Seolgaeng and developed high eating quality brown rice breeding lines by applying marker-assisted backcrossing (MABC) breeding programs to backcross populations between Samgwang and Seolgaeng using KASP markers. SNP markers for foreground selection were identified to improve eating and processability through SNP mapping of Samgwang and Seolgaeng with SSIIa as a target gene in this study. Line selection according to genotype of KASP markers was successful in BC1F1 and BC2F1 generations, with the recurrent parent genome recovery ratio ranging from 91.22% to 98.65%. In BC2F1 seeds of the selected lines, thickness of the aleurone layer was found to range from 13.82 to 21.67 µm, which is much thinner than the 30.91 µm of the wild type, suggesting that selection by MABc could be used as an additional breeding material for the development of highly processed rice varieties. These lines will be useful to develop new brown rice varieties with softer chewing characteristics and processability in rice grain.

Clinical Implications of Shared Epitope and Anti-citrullinated Peptide Antibody in Patients With Rheumatoid Arthritis.

Objective: The shared epitope (SE) and anti-citrullinated peptide antibody (ACPA) are involved in the pathogenesis of rheumatoid arthritis (RA). This study evaluated the clinical implications of SE and ACPA in terms of disease manifestation and response to biologic disease modifying anti-rheumatic drugs (DMARDs). Methods: Patients with identified human leukocyte antigen (HLA)-DRB1 alleles were included to compare the clinical characteristics and drug survival rate of tumor necrosis factor (TNF) inhibitors or abatacept based on the presence of SE and ACPA. Results: Of the 533 patients with identified HLA-DRB1 alleles, 329 patients (61.7%) with SE alleles showed higher disease activity and erosive changes compared to patients without SE alleles. SE-positive patients were more likely to start biologic (b-) or targeted synthetic DMARDs (tsDMARDs) within the first 5 years (p=0.020). The presence of SE, smoking, dyslipidemia, and higher erythrocyte sedimentation rate were independently associated with the initiation of b- or tsDMARDs (p=0.016, 0.028, 0.031, and 0.001, respectively). The presence of SE and ACPA did not affect the drug survival rate of TNF inhibitors, whereas the abatacept retention rate was higher in ACPA-positive patients (p=0.024). Conclusion: The presence of SE affected disease characteristics and prognosis in Korean patients with RA without a significant impact on drug survival rate of TNF inhibitors and abatacept. ACPA positivity was associated with abatacept drug retention, suggesting that abatacept may be helpful in ACPA-positive patients than in ACPA-negative patients.

Use of Disease-modifying Antirheumatic Drugs After Cancer Diagnosis in Rheumatoid Arthritis Patients.

Objective: There is no recommendation for the use of disease-modifying antirheumatic drugs (DMARDs) in patients with rheumatoid arthritis (RA) who developed cancer. We examined changes in the DMARDs prescription patterns associated with cancer diagnosis in RA patients. Methods: We reviewed the medical records of 2,161 RA patients who visited rheumatology clinic between January 2008 and February 2017 and found 40 patients who developed cancer during RA treatment. In these patients, we examined DMARDs prescription patterns before and right after cancer diagnosis and at recent outpatient clinic visits. Results: Before cancer diagnosis, methotrexate (MTX)-combined conventional synthetic DMARDs (csDMARDs) were most commonly prescribed (22, 55.0%) and biological DMARDs (biologics) in nine patients (22.5%). For cancer treatment, 19 patients received chemotherapy (including adjuvant chemotherapy) and 21 patients had surgery only. Right after cancer diagnosis, changes in the DMARDs prescription patterns were similar in discontinuation (13, 32.5%), switching (14, 35.0%), and maintenance (13, 32.5%). DMARDs were discontinued more frequently in the chemotherapy group (9/19, 47.4%) than the surgery only group (4/2, 19.0%) (p<0.05). Among the 13 patients who discontinued DMARDs, nine (69.2%) resumed DMARDs after a median of 5.5 months (interquartile range [IQR] 2.9, 18.3) due to arthritis flare. At a median of 4.6 years (IQR 3.3, 6.7) after cancer diagnosis, 25 patients were evaluated at recent outpatient clinic visits. Four patients received no DMARD, three MTX monotherapies, 11 csDMARDs combination therapies, and seven biologics. Conclusion: A significant number of RA patients who developed cancer during RA treatment were still receiving DMARDs including biologics after cancer diagnosis.

Integrative analysis of lung molecular signatures reveals key drivers of systemic sclerosis-associated interstitial lung disease.

OBJECTIVES: Interstitial lung disease is a significant comorbidity and the leading cause of mortality in patients with systemic sclerosis. Transcriptomic data of systemic sclerosis-associated interstitial lung disease (SSc-ILD) were analysed to evaluate the salient molecular and cellular signatures in comparison with those in related pulmonary diseases and to identify the key driver genes and target molecules in the disease module. METHODS: A transcriptomic dataset of lung tissues from patients with SSc-ILD (n=52), idiopathic pulmonary fibrosis (IPF) (n=549), non-specific interstitial pneumonia (n=49) and pulmonary arterial hypertension (n=81) and from normal healthy controls (n=331) was subjected to filtration of differentially expressed genes, functional enrichment analysis, network-based key driver analysis and kernel-based diffusion scoring. The association of enriched pathways with clinical parameters was evaluated in patients with SSc-ILD. RESULTS: SSc-ILD shared key pathogenic pathways with other fibrosing pulmonary diseases but was distinguishable in some pathological processes. SSc-ILD showed general similarity with IPF in molecular and cellular signatures but stronger signals for myofibroblasts, which in SSc-ILD were in a senescent and apoptosis-resistant state. The p53 signalling pathway was the most enriched signature in lung tissues and lung fibroblasts of SSc-ILD, and was significantly correlated with carbon monoxide diffusing capacity of lung, cellular senescence and apoptosis. EEF2, EFF2K, PHKG2, VCAM1, PRKACB, ITGA4, CDK1, CDK2, FN1 and HDAC1 were key regulators with high diffusion scores in the disease module. CONCLUSIONS: Integrative transcriptomic analysis of lung tissues revealed key signatures of fibrosis in SSc-ILD. A network-based Bayesian approach provides deep insights into key regulatory genes and molecular targets applicable to treating SSc-ILD.

Integrative analysis of lung molecular signatures reveals key drivers of idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a devastating disease with a high clinical burden. The molecular signatures of IPF were analyzed to distinguish molecular subgroups and identify key driver genes and therapeutic targets. METHODS: Thirteen datasets of lung tissue transcriptomics including 585 IPF patients and 362 normal controls were obtained from the databases and subjected to filtration of differentially expressed genes (DEGs). A functional enrichment analysis, agglomerative hierarchical clustering, network-based key driver analysis, and diffusion scoring were performed, and the association of enriched pathways and clinical parameters was evaluated. RESULTS: A total of 2,967 upregulated DEGs was filtered during the comparison of gene expression profiles of lung tissues between IPF patients and healthy controls. The core molecular network of IPF featured p53 signaling pathway and cellular senescence. IPF patients were classified into two molecular subgroups (C1, C2) via unsupervised clustering. C1 was more enriched in the p53 signaling pathway and ciliated cells and presented a worse prognostic score, while C2 was more enriched for cellular senescence, profibrosing pathways, and alveolar epithelial cells. The p53 signaling pathway was closely correlated with a decline in forced vital capacity and carbon monoxide diffusion capacity and with the activation of cellular senescence. CDK1/2, CKDNA1A, CSNK1A1, HDAC1/2, FN1, VCAM1, and ITGA4 were the key regulators as evidence by high diffusion scores in the disease module. Currently available and investigational drugs showed differential diffusion scores in terms of their target molecules. CONCLUSIONS: An integrative molecular analysis of IPF lungs identified two molecular subgroups with distinct pathobiological characteristics and clinical prognostic scores. Inhibition against CDKs or HDACs showed great promise for controlling lung fibrosis. This approach provided molecular insights to support the prediction of clinical outcomes and the selection of therapeutic targets in IPF patients.

Switching from TNFα inhibitor to tacrolimus as maintenance therapy in rheumatoid arthritis after achieving low disease activity with TNFα inhibitors and methotrexate: 24-week result from a non-randomized, prospective, active-controlled trial.

BACKGROUND: Tapering or stopping biological disease-modifying anti-rheumatic drugs has been proposed for patients with rheumatoid arthritis (RA) in remission, but it frequently results in high rates of recurrence. This study evaluates the efficacy and safety of tacrolimus (TAC) as maintenance therapy in patients with established RA in remission after receiving combination therapy with tumor necrosis factor inhibitor (TNFi) and methotrexate (MTX). METHODS: This 24-week, prospective, open-label trial included patients who received TNFi and MTX at stable doses for ≥24 weeks and had low disease activity (LDA), measured by Disease Activity Score-28 for ≥12 weeks. Patients selected one of two arms: maintenance (TNFi plus MTX) or switched (TAC plus MTX). The primary outcome was the difference in the proportion of patients maintaining LDA at week 24, which was assessed using a logistic regression model. Adverse events were monitored throughout the study period. RESULTS: In efficacy analysis, 80 and 34 patients were included in the maintenance and switched arms, respectively. At week 24, LDA was maintained in 99% and 91% of patients in the maintenance and switched arms, respectively (odds ratio, 0.14; 95% confidence interval, 0.01-1.59). Drug-related adverse effects tended to be more common in the switched arm than in the maintenance arm (20.9% versus 7.1%, respectively) but were well-tolerated. CONCLUSION: This controlled study tested a novel treatment strategy of switching from TNFi to TAC in RA patients with sustained LDA, and the findings suggested that TNFi can be replaced with TAC in most patients without the patients experiencing flare-ups for at least 24 weeks. TRIAL REGISTRATION: Korea CDC CRIS, KCT0005868 . Registered 4 February 2021-retrospectively registered.

Clinical phenotype with high risk for initiation of biologic therapy in rheumatoid arthritis: a data-driven cluster analysis.

OBJECTIVES: The clinical manifestations and treatment outcome in patients with rheumatoid arthritis (RA) are heterogeneous. We classified RA patients into subgroups with distinct phenotypes through unsupervised clustering and evaluated the utility of this subclassification for evaluation of clinical outcome. METHODS: A total of 1,103 patients with RA were clustered in an unbiased manner using a k-means clustering method, based on their clinical and phenotypic profiles. Initiation of biological disease-modifying anti-rheumatic drugs (bDMARDs) was evaluated in the segregated clusters to investigate the differential clinical course of each cluster. RESULTS: Patients with RA were classified into four clusters, each with distinct phenotypes. The key features for subclassification were sex, smoking, hypertension, and dyslipidaemia. Cluster 1 consisted of male smokers, who were most likely to initiate bDMARDs by 30 months (p=0.04). Multivariate analysis revealed that overweight, smoking, erythrocyte sedimentation rate, autoantibodies of high titre, and disease activity were the independent predictors of bDMARD initiation at 30 months. Cluster 1 was the highest or the second highest for these independent predictors, suggesting that cluster 1 contained a high-risk group for early initiation of bDMARDs. CONCLUSIONS: The unsupervised clustering of RA patients demonstrated the feasibility of the novel subclassification with respect to predicting clinical outcome. Identifying high-risk patients by a combination of clinical parameters may be useful for the management of RA.

Influenza infection as a trigger for systemic lupus erythematosus flares resulting in hospitalization.

In patients with systemic lupus erythematosus (SLE), there are concerns that infections may increase the risk of flares. We evaluated the association between influenza infection and SLE flares resulting in hospitalization. SLE flares resulting in hospitalization and influenza cases were ascertained from the Korean national healthcare insurance database (2014-2018). We used a self-controlled case series design. We defined the risk interval as the first 7 days after the influenza index date and the control interval was defined as all other times during the observation period of each year. We estimated the incidence rates of SLE flares resulting in hospitalization during the risk interval and control interval and compared them using a Poisson regression model. We identified 1624 influenza infections among the 1455 patients with SLE. Among those, there were 98 flares in 79 patients with SLE. The incidence ratio (IR) for flares during the risk interval as compared with the control interval was 25.75 (95% confidence interval 17.63-37.59). This significantly increased the IRs for flares during the risk interval in both women (IR 27.65) and men (IR 15.30), all age groups (IR 17.00-37.84), with and without immunosuppressive agent (IR 24.29 and 28.45, respectively), and with and without prior respiratory diseases (IR 21.86 and 26.82, respectively). We found significant association between influenza infection and SLE flares resulting in hospitalization. Influenza infection has to be considered as a risk factor for flares in all SLE patients regardless of age, sex, medications, and comorbidities.

Risk factors for cervical spine instability in patients with rheumatoid arthritis.

INTRODUCTION: Cervical spine (C-spine) instability is a unique and significant characteristic of rheumatoid arthritis (RA) because its occurrence is not rare and it can cause compressive cervical myelopathy, which may lead to serious neurologic sequelae. This study evaluated the prevalence and risk factors of C-spine instabilities in RA patients with a focus on anti-citrullinated protein antibody (ACPA) and biologic disease-modifying antirheumatic drug (DMARD) therapies. METHODS: The presence of C-spine instabilities in 1114 patients with RA was evaluated using C-spine radiographies according to the defined metrics. Multivariable logistic regression analyses were performed to identify independent predictors of C-spine instability. The initiation of biologic DMARDs was assessed via a Kaplan-Meier analysis and compared using log-rank tests. RESULTS: In total, 306 (27.5%) patients presented with C-spine instabilities. The most common type was atlantoaxial subluxation (AAS; n = 199 [17.9%]). Male sex, positivity for rheumatoid factor and ACPA, erosive change in the peripheral joints, and presence of osteoporosis were independently associated with C-spine instabilities (all P < 0.05). In particular, positivity for ACPA was the most powerful risk factor (odds ratio: 2.33 [95% confidence interval: 1.37, 3.96], P = 0.002), and it was closely associated with AAS. Patients with AAS were at a higher risk for early initiation of biologic DMARDs. CONCLUSIONS: Positivity for ACPA was a significant risk factor for C-spine instability, and AAS was remarkably correlated to the early initiation of biologic DMARDs, a surrogate index of poor long-term outcomes. Key Points • The presence of antibodies against citrullinated proteins was a strong risk factor for C-spine instability in patients with rheumatoid arthritis. • Atlantoaxial subluxation was significantly associated with early initiation of biologic DMARDs, a surrogate index of poor long-term outcome.

Novel classification of axial spondyloarthritis to predict radiographic progression using machine learning.

OBJECTIVES: Prediction and determination of drug efficacy for radiographic progression is limited by the heterogeneity inherent in axial spondyloarthritis (axSpA). We investigated whether unbiased clustering analysis of phenotypic data can lead to coherent subgroups of axSpA patients with a distinct risk of radiographic progression. METHODS: A group of 412 patients with axSpA was clustered in an unbiased way using a agglomerative hierarchical clustering method, based on their phenotype mapping. We used a generalised linear model, naïve Bayes, Decision Trees, K-Nearest-Neighbors, and Support Vector Machines to construct a consensus classification method. Radiographic progression over 2 years was assessed using the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS). RESULTS: axSpA patients were classified into three distinct subgroups with distinct clinical characteristics. Sex, smoking, HLA-B27, baseline mSASSS, uveitis, and peripheral arthritis were the key features that were found to stratifying the phenogroups. The three phenogroups showed distinct differences in radiographic progression rate (p<0.05) and the proportion of progressors (p<0.001). Phenogroup 2, consisting of male smokers, had the worst radiographic progression, while phenogroup 3, exclusively suffering from uveitis, showed the least radiographic progression. The axSpA phenogroup classification, including its ability to stratify risk, was successfully replicated in an independent validation group. CONCLUSIONS: Phenotype mapping results in a clinically relevant classification of axSpA that is applicable for risk stratification. Novel coupling between phenotypic features and radiographic progression can provide a glimpse into the mechanisms underlying divergent and shared features of axSpA.

Association of influenza infection with hospitalisation-related systemic lupus erythematosus flares: a time series analysis.

OBJECTIVES: This study aimed to investigate whether the influenza annual outbreak in Korea is related to hospitalisation-related flares in systemic lupus erythematosus (SLE) patients. METHODS: The weekly frequency of hospitalisation-related SLE flares (2012-2015) was collected from the Korean National Health Insurance claim database. The weekly laboratory-confirmed detection rate of influenza infection was obtained from the Korea Centers for Disease Control and Prevention database. A generalised linear model was used to examine the relative risks (RRs) of hospitalisation-related SLE flares associated with influenza infection, after adjusting for time trends and meteorological data. RESULTS: A total of 2,223 hospitalisation-related SLE flares were analysed. An interquartile range (24.5%) increase in influenza infection was associated with a 14.0% increase in hospitalisation-related SLE flares (RR, 1.14; 95% confidence interval [CI]: 1.04-1.25; p=0.006). In addition, influenza infections at lag 0-1 (over 2 weeks including concurrent and 1 previous week) and lag 0-2 (over 3 weeks including concurrent and 2 previous weeks) were associated with increase in hospitalisation-related SLE flares (RR, 1.14; 95% confidence interval [CI]: 1.03-1.26; p=0.014 and RR, 1.13; 95% CI: 1.02-1.26; p=0.023). Significant associations were especially observed in women (RR, 1.15; 95% CI: 1.15-1.16; p=0.006) and immunosuppressant (RR, 1.26; 95% CI: 1.26-1.27; p<0.001) or glucocorticoid recipients (RR, 1.17, 95% CI: 1.16-1.17; p=0.004). CONCLUSIONS: This study shows a significant association between seasonal influenza infection and flares in SLE patients, which suggests influenza can be a novel environmental risk factor for SLE flares.

Deep phenotyping of synovial molecular signatures by integrative systems analysis in rheumatoid arthritis.

OBJECTIVE: RA encompasses a complex, heterogeneous and dynamic group of diseases arising from molecular and cellular perturbations of synovial tissues. The aim of this study was to decipher this complexity using an integrative systems approach and provide novel insights for designing stratified treatments. METHODS: An RNA sequencing dataset of synovial tissues from 152 RA patients and 28 normal controls was imported and subjected to filtration of differentially expressed genes, functional enrichment and network analysis, non-negative matrix factorization, and key driver analysis. A naïve Bayes classifier was applied to the independent datasets to investigate the factors associated with treatment outcome. RESULTS: A matrix of 1241 upregulated differentially expressed genes from RA samples was classified into three subtypes (C1-C3) with distinct molecular and cellular signatures. C3 with prominent immune cells and proinflammatory signatures had a stronger association with the presence of ACPA and showed a better therapeutic response than C1 and C2, which were enriched with neutrophil and fibroblast signatures, respectively. C2 was more occupied by synovial fibroblasts of destructive phenotype and carried highly expressed key effector molecules of invasion and osteoclastogenesis. CXCR2, JAK3, FYN and LYN were identified as key driver genes in C1 and C3. HDAC, JUN, NFKB1, TNF and TP53 were key regulators modulating fibroblast aggressiveness in C2. CONCLUSIONS: Deep phenotyping of synovial heterogeneity captured comprehensive and discrete pathophysiological attributes of RA regarding clinical features and treatment response. This result could serve as a template for future studies to design stratified approaches for RA patients.

Pregnancy rates and perinatal outcomes in women with systemic lupus erythematosus: data from the Korean national health claims database.

INTRODUCTION/OBJECTIVES: The pregnancy rate in systemic lupus erythematosus (SLE) is not fully understood and comparisons of adverse pregnancy outcomes (APOs) with SLE versus the general population are limited. This study aimed to estimate the pregnancy rate and APOs in Korean SLE compared to those without SLE. METHOD: Pregnant women were identified using the ICD-10 codes for delivery and abortion in the Korean national health claims database (2013-2015). APOs were classified as fetal loss, intrauterine growth retardation (IUGR), pre-eclampsia/eclampsia, and gestational diabetes. Annual incidence rates (IRs) of pregnancy and APOs were calculated in women with SLE and the general population without SLE and the two groups were compared using age-adjusted incidence rate ratios (IRRs). Age-stratified IRRs were further analyzed. RESULTS: The annual IRs of pregnancy in SLE were 29.54-30.70 per 1000 persons. The IRRs were lower in women with SLE than in the general population: 0.68 (0.61-0.76), 0.66 (0.60-0.74), and 0.74 (0.66-0.82) in each respective year. The IRRs of fetal loss, IUGR, and pre-eclampsia/eclampsia were 1.30 (1.14-1.49), 4.65 (3.55-6.09), and 3.43 (2.70-4.36), respectively. However, the IRR of gestational diabetes in SLE did not significantly differ from that of women without SLE. Among the APOs, fetal loss, IUGR, and pre-eclampsia/eclampsia showed decreasing tendencies as age increased. CONCLUSIONS: Pregnancy rates in SLE were approximately 30% lower than those in the general population. Except for gestational diabetes, fetal loss, IUGR, and pre-eclampsia/eclampsia were higher in SLE and showed a decreasing tendency with age. Key Points • This population-based cohort study showed that pregnancy rates in SLE were approximately 30% lower than those in the general population. • SLE had a 1.3-fold higher rate of fetal loss, more than 4-fold higher IUGR rate, and more than 3-fold pre-eclampsia or eclampsia rate compared with the general population. • Adverse pregnancy outcomes in SLE showed a decreasing tendency with age.

Twelve Weeks of Strengthening Exercise for Patients with Rheumatoid Arthritis: A Prospective Intervention Study.

Rheumatoid arthritis (RA) patients may benefit from exercise for several reasons. However, whole-limb strengthening exercises for such patients remain poorly studied. We hypothesized that systemic strength training that includes the upper and lower extremities would improve strength per se and enhance the quality of life. Here, we investigated the effects of 12 weeks of upper- and lower-limb strengthening exercise on the strength and quality of life of RA patients using the International Classification of Functioning, Disability, and Health model. This was a prospective, interventional controlled trial. Forty female RA patients were recruited and assigned to two groups not based on willingness to exercise, with 20 patients in the exercise group and 20 in the control group. All patients in the exercise group received once-weekly training sessions of 60 min over 12 weeks. All participants were assessed before and after the 12-week intervention period. We measured the hand grip strength and isometric quadriceps contraction, the cross-sectional area of the rectus femoris (CSA-RF) (via ultrasonography), and performed the 30 s sit-to-stand test and the 6 min walk test (6MWT). We derived the Borg scale score after the 6MWT and assessed the extent of social participation and quality of life using a Korean version of the 36-Item Short Form Health Survey (SF-36). A total of 35 subjects completed the experiment (18 in the exercise group, 17 in the control group). After the 12-week intervention period, the lower-limb strength and the CSA-RF were significantly increased in the exercise group. The activity level did not change significantly in either group. The exercise group exhibited significant improvements in the SF-36 mental health domain scores. Thus, strengthening exercise is useful for patients with RA.

Network-based modeling of drug effects on disease module in systemic sclerosis.

The network-based proximity between drug targets and disease genes can provide novel insights regarding the repercussions, interplay, and repositioning of drugs in the context of disease. Current understanding and treatment for reversing of the fibrotic process is limited in systemic sclerosis (SSc). We have developed a network-based analysis for drug effects that takes into account the human interactome network, proximity measures between drug targets and disease-associated genes, genome-wide gene expression and disease modules that emerge through pertinent analysis. Currently used and potential drugs showed a wide variation in proximity to SSc-associated genes and distinctive proximity to the SSc-relevant pathways, depending on their class and targets. Tyrosine kinase inhibitors (TyKIs) approach disease gene through multiple pathways, including both inflammatory and fibrosing processes. The SSc disease module includes the emerging molecular targets and is in better accord with the current knowledge of the pathophysiology of the disease. In the disease-module network, the greatest perturbing activity was shown by nintedanib, followed by imatinib, dasatinib, and acetylcysteine. Suppression of the SSc-relevant pathways and alleviation of the skin fibrosis was remarkable in the inflammatory subsets of the SSc patients receiving TyKI therapy. Our results show that network-based drug-disease proximity offers a novel perspective into a drug's therapeutic effect in the SSc disease module. This could be applied to drug combinations or drug repositioning, and be helpful guiding clinical trial design and subgroup analysis.