Background: This study aimed to investigate the association between levels of serum amyloid A (SAA) and the activity of systemic lupus erythematosus (SLE). Material/Methods: The study included 135 patients with SLE, including 52 patients with active SLE and 83 patients with inactive SLE and 149 healthy controls. The degree of activity of SLE was assessed using the SLE Disease Activity Index 2000 (SLEDAI-2K). Serum SAA levels were measured using a Cobas 8000 c702 modular analyzer. Results: The levels of SAA were significantly increased in patients with active SLE compared with patients with inactive SLE (median IQR, 16.65 mg/L; range, 9.35-39.68 mg/L, and median IQR, 2.30 mg/L, range, 1.30-4.80 mg/L) (p<0.001). Levels of SAA were significantly correlated with the SLEDAI-2K scores, the erythrocyte sedimentation rate (ESR), and hypersensitive C-reactive protein (Hs-CRP) in patients with SLE (r=0.726, p<0.001; r=0.631, p<0.001; r=0.774, p<0.001, respectively). Multivariate logistic regression analysis showed that the SAA values were independently associated with active SLE when controlled for white blood cell (WBC) count, red blood cell distribution width (RDW), ESR, and Hs-CRP (OR=1.772; p=0.01; 95% CI, 1.101-2.851). Receiver operating characteristic (ROC) curve analysis for SAA was used to identify patients with active SLE with an area under the curve of 0.971, a sensitivity of 90.4%, and a specificity of 94.0%. Conclusions: SAA levels were significantly correlated with disease activity in patients with SLE.

Objective. To construct a novel nomogram model that predicts the risk of diabetic nephropathy (DN) incidence in Chinese patients with type 2 diabetes mellitus (T2DM). Methods. Questionnaire surveys, physical examinations, routine blood tests, and biochemical index evaluations were conducted on 1095 patients with T2DM from Guilin. A least absolute contraction selection operator (LASSO) regression and multivariable logistic regression analysis were used to screen out DN risk factors. A logistic regression analysis incorporating the screened risk factors was used to establish a predictive nomogram model. The performance of the nomogram model was evaluated using the C-index, an area under the receiver operating characteristic curve (AUC), calibration plots, and a decision curve analysis. Bootstrapping was applied for internal validation. Results. Independent predictors for DN incidence risk included gender, age, hypertension, medicine use, duration of diabetes, body mass index, blood urea nitrogen level, serum creatinine level, neutrophil to lymphocyte ratio, and red blood cell distribution width. The nomogram model exhibited moderate prediction ability with a C-index of 0.819 (95% confidence interval (CI): 0.783–0.853) and an AUC of 0.813 (95%CI: 0.778–0.848). The C-index from internal validation reached 0.796 (95%CI: 0.763–0.829). The decision curve analysis displayed that the DN risk nomogram was clinically applicable when the risk threshold was between 1 and 83%. Conclusion. Our novel and simple nomogram containing 10 factors may be useful in predicting DN incidence risk in T2DM patients.