Objective To construct a risk prediction model for atherosclerosis(AS)in patients with rheumatoid arthritis(RA)based on Lasso-Logistic regression analysis and provide a scientific basis for individualized clinical intervention.Methods The retrospective clinical data were collected from 344 RA patients,including 86 patients with AS(RA+AS group)and 258 patients with without AS(RA group).The clinical characteristics and initial laboratory test results were compared between the two groups.Lasso regression was used to screen the key predictive variables,and Logistic regression was combined to construct the prediction mode.The discrimination of the model was evaluated through the receiver operating characteristic(ROC)curve and the area under the curve(AUC).The Hosmer-Lemeshow test was used to assess the calibration,and decision curve analysis was used to verify the clinical applicability of the model.Results Seven predictive variables were identified including RA disease duration,DAS28 score,C-reactive protein(CRP),triglycerides(TG),high-density lipoprotein cholesterol(HDL-C),fasting blood glucose(FBG)and hypertension.The risk prediction model for AS in RA patients was:Logit(P)=-2.674+0.605×RA disease duration+0.393×DAS28 score+0.310×CRP+1.346×TG-2.289×HDL-C+0.679×FBG+0.711×hypertension.The AUC of the model was 0.965(95%CI:0.943-0.987),and the Hosmer-Lemeshow test showed χ2=0.547,P=1.000,indicating good discrimination and calibration.Clinical decision curve analysis showed that the probability threshold ranged from 7%to 92%,demonstrating high clinical applicability.Conclusion The AS risk prediction model constructed in this study for RA patients can effectively identify high-risk individuals,supporting the development of personalized prevention and treatment strategies.

Objective To construct a risk prediction model for atherosclerosis(AS)in patients with rheumatoid arthritis(RA)based on Lasso-Logistic regression analysis and provide a scientific basis for individualized clinical intervention.Methods The retrospective clinical data were collected from 344 RA patients,including 86 patients with AS(RA+AS group)and 258 patients with without AS(RA group).The clinical characteristics and initial laboratory test results were compared between the two groups.Lasso regression was used to screen the key predictive variables,and Logistic regression was combined to construct the prediction mode.The discrimination of the model was evaluated through the receiver operating characteristic(ROC)curve and the area under the curve(AUC).The Hosmer-Lemeshow test was used to assess the calibration,and decision curve analysis was used to verify the clinical applicability of the model.Results Seven predictive variables were identified including RA disease duration,DAS28 score,C-reactive protein(CRP),triglycerides(TG),high-density lipoprotein cholesterol(HDL-C),fasting blood glucose(FBG)and hypertension.The risk prediction model for AS in RA patients was:Logit(P)=-2.674+0.605×RA disease duration+0.393×DAS28 score+0.310×CRP+1.346×TG-2.289×HDL-C+0.679×FBG+0.711×hypertension.The AUC of the model was 0.965(95%CI:0.943-0.987),and the Hosmer-Lemeshow test showed χ2=0.547,P=1.000,indicating good discrimination and calibration.Clinical decision curve analysis showed that the probability threshold ranged from 7%to 92%,demonstrating high clinical applicability.Conclusion The AS risk prediction model constructed in this study for RA patients can effectively identify high-risk individuals,supporting the development of personalized prevention and treatment strategies.

Objective:To investigate the relationship between serum homocysteine(Hcy) and osteoporosis in patients with systemic lupus erythematosus (SLE).Methods:A total of 105 SLE patients were selected in Shaoxing Central Hospital from December 2017 to May 2018. According to the risk of osteoporotic fracture, they were divided into two groups: low-risk group(69 patients), middle-high-risk group (36 patients). After anti-osteoporosis treatment for 12 months, the relationship between bone mineral density (BMD), T value and serum Hcy level were analyzed.Results:The level of BMD of both groups increased after treatment, and the difference was statistically significant ( P<0.05). In low-risk group, the T value of lumbar spine increased significantly after treatment ( P<0.05). the T value of neck of femur had an upward trend, but the difference was not statistically significant ( P>0.05). In middle-high-risk group, the T values of lumbar vertebrae and femoral neck were significantly higher after treatment ( P<0.05). There was significant difference in serum Hcy between middle-high-risk group and low-risk group before treatment: (15.42 ± 4.13) μmol/L vs. (13.52 ± 3.12) μmol/L, P<0.05. The level of serum Hcy in both groups decreased after treatment: (12.14 ± 3.17) μmol/L vs. (13.52 ± 3.12) μmol/L, (13.73 ± 3.22) μmol/L vs. (15.42 ± 4.13) μmol/L, and the differences were statistically significant ( P< 0.05). The serum Hcy level was negatively correlated with both bone mineral density and T value ( P<0.05). Positive correlation was observed between serum Hcy level and fracture risk ( P<0.05). Conclusions:Serum Hcy is negatively correlated with osteoporosis in SLE patients and declines significantly after anti-osteoporosis treatment. Hcy is expected to be a promising biomarker for secondary osteoporosis in patients with autoimmune system diseases.