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Aim: Tripterygium wilfordii Hook F (TwHF) has been shown to substantially reduce proteinuria in patients with diabetic kidney disease (DKD); however, the effect of TwHF on renal outcomes in DKD remains unknown. Accordingly, we aimed to establish the effects of TwHF on renal outcomes in patients with DKD., Methods: Overall, 124 patients with DKD, induced by type 2 diabetes mellitus, with 24-h proteinuria > 2 g, and an estimated glomerular filtration rate > 30 mL/min/1.73 m 2 were retrospectively investigated. The renal outcomes were defined as doubling serum creatinine levels or end-stage kidney disease. Kaplan-Meier curves and Cox regression analyses were performed to analyze prognostic factors for renal outcomes., Results: By the end of the follow-up, renal outcomes were observed in 23 and 11 patients in the non-TwHF and TwHF groups, respectively ( p  = 0.006). TwHF significantly reduced the risk of renal outcomes (adjusted hazard ratio [HR] 0.271, 95% confidence interval [CI] 0.111-0.660, p  = 0.004) in patients with chronic kidney disease (CKD) G3 (adjusted HR 0.274, 95%CI 0.081-0.932, p  = 0.039). Based on the Kaplan-Meier analysis, 1- and 3-year proportions of patients without renal outcomes were significantly lower in the non-TwHF group than those in the TwHF group (92.8% vs. 95.5% and 47.2% vs. 76.8%, respectively; p  = 0.0018)., Conclusion: In DKD patients with severe proteinuria, TwHF could prevent DKD progression, especially in patients with CKD G3. A randomized clinical trial is needed to elucidate the benefits of TwHF on renal outcomes in patients with DKD.

Introduction: A good prediction model plays an important role in determining the progression to diabetic kidney disease. We aimed to create a model to predict progression to kidney failure in patients with diabetic kidney disease. Methods: We retrospectively assessed 641 patients with type 2 diabetic kidney disease as derivation cohort and 280 patients as external out time validation cohort. We used a combination of clinical guidance and univariate logistic regression to select the relevant variables. We calculated the discrimination and calibration of different models. The best model was selected according to the optimal combination of discrimination and calibration. Results: During the 3 years follow up, there were 272 outcomes (42%) in derivation cohort and 138 outcomes (49%) in external validation cohort. The final variables selected in the multivariate logistics regression were age, gender, hemoglobin, NLR, serum cystatin C, eGFR, 24-h urine protein, and the use of oral hypoglycemic drugs. We developed four different models as clinical, laboratory, lab-medication, and full models according to these independent risk factors. Laboratory model performed well in both discrimination and calibration among all the models (C-statistics: external validation 0.863; p value of the Hosmer-Lemeshow, .817). There was no significant difference in NRI among laboratory model, lab-medication model, and full model ( p  > .05). So, we chose the laboratory model as the optimal model. Conclusion: We constructed a nomogram which contained hemoglobin, NLR, serum cystatin C, eGFR, and 24-h urine protein to predict the risk of patients with diabetic kidney disease initiating renal replacement in 3 years.