The Utility of Radiographic Focal Erosions of Hands or Feet in Predicting DXA-defined Osteoporosis of the Hip in Patients with Rheumatoid Arthritis

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

To establish a model for osteoporosis risk in patients with rheumatoid arthritis and validate the model. A newly generated predictive model has been suggested to have good differentiation, calibration, and clinical validity and may be a useful clinical model for predicting osteoporosis in patients with rheumatoid arthritis.

PURPOSE

To establish a prediction model for osteoporosis risk in patients with rheumatoid arthritis and validate the model internally and externally.

METHODS

A total of 270 patients with rheumatoid arthritis who underwent bone mineral density measurement at our hospital from June 2019 to June 2020 were enrolled in the study. The patients were divided into two groups according to their entry time: a training set containing the first 2/3 of the patients (n = 180) and a validation set containing the remaining 1/3 of the patients (n = 90). Binary logistic regression analysis was used to establish the regression models, and the concordance index (C-index), calibration plot, and decision curve analysis were used to evaluate the prediction model.

RESULTS

Five variables, including age (X1), course of disease (X2), the disease activity score using 28 joint counts (DAS28) (X4), anti-cyclic citrullinated peptide antibody (CCP) (X7), and 7-joint ultrasonic bone erosion (X14), were selected to enter the model. The prediction model is Logit Y = - 12.647 + 0.133X1 + 0.011X2 + 0.754X4 + 0.001X7 + 0.605X14. The model had good differentiation; the C-index in the internal verification was 0.947 (95% CI is 0.932-0.977) and the C-index in the external verification was 0.946 (95% CI is 0.940-0.994). The calibration plot of the model showed excellent consistency between the prediction probability and actual probability. When > 0.483 was taken as the cutoff value for the diagnosis of osteoporosis, the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and Jordan index of the model were 90.24%, 87.76%, 7.37, 0.11, and 78.00%, respectively.

CONCLUSION

A newly generated predictive model has been suggested to have good differentiation, calibration, and clinical validity and may be a useful clinical model for predicting osteoporosis in patients with rheumatoid arthritis.

CTLA-4Ig Improves Hyperalgesia in a Mouse Model of Osteoporosis

This study aimed to evaluate skeletal pain associated with osteoporosis and to examine the inhibitory effects of cytotoxic T lymphocyte-associated antigen-4Ig (CTLA-4Ig) administration in ovariectomized (OVX) mice. Eight-week-old female ddY mice were assigned to three groups: sham-operated mice (SHAM) treated with vehicle, OVX mice treated with vehicle (OVX), and OVX mice treated with CTLA-4Ig (CTLA-4Ig). Vehicle or CTLA-4Ig was injected intraperitoneally, starting immediately after surgery. After 4 weeks of treatment, mechanical sensitivity was examined, and the bilateral hind limbs were removed and evaluated by micro-computed tomography, immunohistochemical analyses, and messenger RNA expression analysis. Ovariectomy induced bone loss and mechanical hyperalgesia in the hindlimbs. CTLA-4Ig treatment prevented bone loss in the hindlimbs compared to vehicle administration in the OVX group. Moreover, mechanical hyperalgesia was significantly decreased in the CTLA-4Ig treatment group in comparison to the OVX group. The expression levels of tumor necrosis factor-α (TNF-α) and sclerostin (SOST), as well as the number of osteoclasts, were increased, and the expression level of Wnt-10b was decreased in the OVX group compared with the SHAM group, whereas these parameters were improved in the CTLA-4Ig group compared with the OVX group. The novelty of this research is that CTLA-4Ig administration prevented bone loss and mechanical hyperalgesia induced by ovariectomy in the hindlimbs.