Establishment and verification of an osteoporosis risk model in patients with rheumatoid arthritis: a valuable new model

Systemic complications of rheumatoid arthritis: Focus on pathogenesis and treatment

As a systemic autoimmune disease, rheumatoid arthritis (RA) usually causes damage not only to joints, but also to other tissues and organs including the heart, kidneys, lungs, digestive system, eyes, skin, and nervous system. Excessive complications are closely related to the prognosis of RA patients and even lead to increased mortality. This article summarizes the serious complications of RA, focusing on its incidence, pathogenesis, clinical features, and treatment methods, aiming to provide a reference for clinicians to better manage the complications of RA.

A Predictive Model for Abnormal Bone Density in Male Underground Coal Mine Workers

The dark and humid environment of underground coal mines had a detrimental effect on workers’ skeletal health. Optimal risk prediction models can protect the skeletal health of coal miners by identifying those at risk of abnormal bone density as early as possible. A total of 3695 male underground workers who attended occupational health physical examination in a coal mine in Hebei, China, from July to August 2018 were included in this study. The predictor variables were identified through single-factor analysis and literature review. Three prediction models, Logistic Regression, CNN and XG Boost, were developed to evaluate the prediction performance. The training set results showed that the sensitivity of Logistic Regression, XG Boost and CNN models was 74.687, 82.058, 70.620, the specificity was 80.986, 89.448, 91.866, the F1 scores was 0.618, 0.919, 0.740, the Brier scores was 0.153, 0.040, 0.156, and the Calibration-in-the-large was 0.104, 0.020, 0.076, respectively, XG Boost outperformed the other two models. Similar results were obtained for the test set and validation set. A two-by-two comparison of the area under the ROC curve (AUC) of the three models showed that the XG Boost model had the best prediction performance. The XG Boost model had a high application value and outperformed the CNN and Logistic regression models in prediction.

Osteoporosis in patients with rheumatoid arthritis is associated with serum immune regulatory cellular factors

OBJECTIVE

Osteoporosis (OP) is a comorbidity of rheumatoid arthritis (RA) that largely causes disability. This study discussed the expression patterns of serum immunoregulatory factors and their clinical significance in RA patients complicated with OP.

METHODS

A total of 116 RA patients were enrolled. Bone mineral density (BMD) was measured using dual energy X-ray absorptiometry to allocate patients to OP group (N = 62) and non-OP group (N = 54). CRP, rheumatoid factor, IgG, IgA, and IgM were detected using rate nephelometry. Erythrocyte sedimentation rate (ESR) and bone metabolic indexes were detected using Microsed automatic ESR analyzer and Cobas e601 automated immunoassay systems and reagents. IL-17, IL-6, TNF-α, IL-10, and IL-4 levels were determined using ELISA kit and their prediction values on OP were analyzed using the ROC curve. Influencing factors of OP incidence were analyzed using logistic regression model.

RESULTS

RA patients with OP showed increased age, disease course, tender and swollen joints, ESR, CRP, DAS28 scores, β-CTX, IL-17, IL-6, and TNF-α, and decreased 25(OH)D3, IL-10, and IL-4. DAS28 was positively correlated with IL-17, IL-6, and TNF-α, and negatively correlated with IL-10 and IL-4. DAS28, IL-17, IL-10, and IL-4 were independently correlated with OP in RA patients. The combination of DAS28, IL-17, IL-10, and IL-4 can better predict the incidence of OP complication in RA patients.

CONCLUSION

IL-17, IL-6, TNF-α, IL-10, and IL-4 were associated with disease activity of RA patients complicated with OP. A combination of DAS28, IL-17, IL-10, and IL-4 might predict OP incidence in RA patients.

OUP accepted manuscript

Rheumatoid arthritis (RA) and osteoporosis (OP) are two comorbid complex inflammatory conditions with evidence of shared genetic background and causal relationships. We aimed to clarify the genetic architecture underlying RA and various OP phenotypes while additionally considering an inflammatory component, C-reactive protein (CRP). Genome-wide association study summary statistics were acquired from the GEnetic Factors for OSteoporosis Consortium, Cohorts for Heart and Aging Research Consortium and UK Biobank. Mendelian randomization (MR) was used to detect the presence of causal relationships. Colocalization analysis was performed to determine shared genetic variants between CRP and OP phenotypes. Analysis of pleiotropy between traits owing to shared causal single nucleotide polymorphisms (SNPs) was performed using PL eiotropic A nalysis under CO mposite null hypothesis (PLACO). MR analysis was suggestive of horizontal pleiotropy between RA and OP traits. RA was a significant causal risk factor for CRP (β = 0.027, 95% confidence interval = 0.016–0.038). There was no evidence of CRP→OP causal relationship, but horizontal pleiotropy was apparent. Colocalization established shared genomic regions between CRP and OP, including GCKR and SERPINA1 genes. Pleiotropy arising from shared causal SNPs revealed through the colocalization analysis was all confirmed by PLACO. These genes were found to be involved in the same molecular function ‘protein binding’ (GO:0005515) associated with RA, OP and CRP. We identified three major components explaining the epidemiological relationship among RA, OP and inflammation: (1) Pleiotropy explains a portion of the shared genetic relationship between RA and OP, albeit polygenically; (2) RA contributes to CRP elevation and (3) CRP, which is influenced by RA, demonstrated pleiotropy with OP.