Lung illness encountered in patients with rheumatic diseases bears clinical significance in terms of increased morbidity and mortality as well as potential challenges placed on patient care. Although our understanding of natural history of this important illness is still limited, epidemiologic knowledge has been accumulated during the past decade to provide useful information on the risk factors and prognosis of lung involvements in rheumatic diseases. Moreover, the pathogenesis particularly in the context of genetics has been greatly updated for both the underlying rheumatic disease and associated lung involvement. This review will focus on the current update on the epidemiologic and genetics features and treatment options of the lung involvements associated with four major rheumatic diseases (rheumatoid arthritis, systemic sclerosis, myositis, and systemic lupus erythematosus), with more attention to a specific form of involvement or interstitial lung disease.

Background and objective: RA-ILD has a variable clinical course, and its prognosis is difficult to predict. Moreover, risk prediction models for prognosis remain undefined. Methods: The prediction model was developed using retrospective data from 153 patients with RA-ILD and validated in an independent RA-ILD cohort (n = 149). Candidate variables for the prediction models were screened using a multivariate Cox proportional hazard model. C-statistics were calculated to assess and compare the predictive ability of each model. Results: In the derivation cohort, the median follow-up period was 54 months, and 38.6% of the subjects exhibited a UIP pattern on HRCT imaging. In multivariate Cox analysis, old age (≥60 years, HR: 2.063), high fibrosis score (≥20% of the total lung extent, HR: 4.585), a UIP pattern (HR: 1.899) and emphysema (HR: 2.596) on HRCT were significantly poor prognostic factors and included in the final model. The prediction model demonstrated good performance in the prediction of 5-year mortality (C-index: 0.780, P < 0.001); furthermore, patients at risk were divided into three groups with 1-year mortality rates of 0%, 5.1% and 24.1%, respectively. Predicted and observed mortalities at 1, 2 and 3 years were similar in the derivation cohort, and the prediction model was also effective in predicting prognosis of the validation cohort (C-index: 0.638, P < 0.001). Conclusion: Our results suggest that a risk prediction model based on HRCT variables could be useful for patients with RA-ILD.

The aim of our observational study was to investigate the clinical significance of interleukin (IL)-34, a novel osteoclastogenic cytokine, for predicting structural damage in patients with rheumatoid arthritis (RA). Serum IL-34 levels were measured in 100 RA patients, 36 patients with ankylosing spondylitis (AS), and 59 gender- and age-matched healthy individuals using an enzyme-linked immunosorbent assay. We also measured IL-34 concentrations in synovial fluid (SF) samples from 18 RA patients and 19 osteoarthritis (OA) patients. Progression of structural damage was assessed in 81 patients with RA by plain radiographs using the modified Sharp/van der Heijde score (SHS) at baseline and after an average 1.6-year follow-up period. Serum IL-34 levels were significantly higher in patients with RA (p < 0.001) or AS (p < 0.001) than in healthy controls. SF IL-34 levels were also significantly higher in RA patients than in OA patients (p < 0.001). In RA, serum IL-34 levels were associated with rheumatoid factor positivity (p = 0.01), current smoking (p < 0.01), erythrocyte sedimentation rate (p = 0.01), and C-reactive protein levels (p < 0.01), but not with disease activity score 28. ΔSHS/year was positively correlated with serum IL-34 levels (r = 0.443, p < 0.001). In multivariate logistic regression analyses, serum IL-34 level was an independent risk factor for radiographic progression. These results suggest that IL-34, a novel osteoclastogenic cytokine, plays a role in RA-associated joint damage and is a potential biomarker for predicting subsequent radiographic progression in patients with RA.

Our study aimed to investigate whether serum leucine-rich alpha-2-glycoprotein (LRG) levels are elevated in patients with rheumatoid arthritis (RA). In addition, we assessed their correlation with disease activity parameters and pro-inflammatory cytokine, tumor necrosis factor-α (TNF-α). Our study included 69 patients with RA and 48 age- and sex-matched healthy controls. Serum concentrations of TNF-α and LRG were determined by enzyme-linked immunosorbent assay. Serum LRG concentrations were significantly elevated in patients with RA compared with those in healthy controls (30.8±14.4 vs. 22.2±6.1 ng/mL; P<0.001). In patients with RA, serum LRG levels were found to be correlated with disease activity score 28 (DAS28), erythrocyte sedimentation rate, and C-reactive protein levels (γ=0.671; γ=0.612; and γ=0.601, P<0.001, respectively), but not with serum TNF-α levels. Serum LRG levels in patients with an active disease status (DAS28≥2.6) were significantly higher than those in remission (DAS28<2.6) (36.45±14.36 vs. 24.63±8.81 ng/mL; P<0.001). Our findings suggest that serum LRG could contribute to the inflammatory process independent of TNF-α and it may be a novel biomarker for assessing inflammatory activity in patients with RA.Graphical Abstract

Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the sacroiliac joints. In this study, we develop a method for detecting bone marrow edema by magnetic resonance (MR) imaging of the sacroiliac joints and a deep-learning network. A total of 815 MR images of the sacroiliac joints were obtained from 60 patients diagnosed with axSpA and 19 healthy subjects. Gadolinium-enhanced fat-suppressed T1-weighted oblique coronal images were used for deep learning. Active sacroiliitis was defined as bone marrow edema, and the following processes were performed: setting the region of interest (ROI) and normalizing it to a size suitable for input to a deep-learning network, determining bone marrow edema using a convolutional-neural-network-based deep-learning network for individual MR images, and determining sacroiliac arthritis in subject examinations based on the classification results of individual MR images. About 70% of the patients and normal subjects were randomly selected for the training dataset, and the remaining 30% formed the test dataset. This process was repeated five times to calculate the average classification rate of the five-fold sets. The gradient-weighted class activation mapping method was used to validate the classification results. In the performance analysis of the ResNet18-based classification network for individual MR images, use of the ROI showed excellent detection performance of bone marrow edema with 93.55 ± 2.19% accuracy, 92.87 ± 1.27% recall, and 94.69 ± 3.03% precision. The overall performance was additionally improved using a median filter to reflect the context information. Finally, active sacroiliitis was diagnosed in individual subjects with 96.06 ± 2.83% accuracy, 100% recall, and 94.84 ± 3.73% precision. This is a pilot study to diagnose bone marrow edema by deep learning based on MR images, and the results suggest that MR analysis using deep learning can be a useful complementary means for clinicians to diagnose bone marrow edema.