Objective To assess the efficacy and safety of Sodium ferulate for diabetic kidney disease. Methods Based on the principles and methods of Cochrane systematic reviews, we searched the Cochrane Central Register of Controlled Trials (Issue 4, 2008), MEDLINE (1996 to December 2008), EMbase (1980 to December 2008), CBMdisc (1990 to December 2008), CNKI (1994 to December 2008) and VIP (1989 to December 2008). And we also hand searched relevant journals and conference proceedings. We evaluated the risk of the bias of the included RCTs according to the Cochrane Handbook for Systematic Reviews of Interventions Version 5.0.1. The Cochrane Collaboration’s software RevMan 5.0 was used for meta-analysis. Results Thirty-nine RCTs were enrolled in the review, including 2 351 patients with type 2 diabetic kidney disease met the inclusion criteria. Most of these trials were small and of low quality with a high risk of bias. The results of meta-analysis showed that ① Sodium ferulate was better on attenuating UAER (WMD=–?42.92, 95%CI –?52.61 to –?33.23), 24 hours urinary protein (WMD=–?0.11, 95%CI –?0.16 to –?0.05), BUN (WMD=–?0.76, 95%CI –?1.04 to –?0.46) and Scr (WMD=–?8.38, 95%CI –?11.81 to –?4.94); Sodium ferulate was better on the regulation of FBG and 2 h-BG of clinical DKD (WMD= –?2.39, 95%CI –?3.23 to –?1.54), but not superior to routine treatment on the improvement of HbA1c (WMD= –?0.12, 95%CI –?0.27 to 0.02) and 2 h-BG of early DKD (WMD= –?0.22, 95%CI –?0.49 to 0.04); Sodium ferulate was better on the improvement of SBP and MAP, however, Sodium ferulate was not superior to routine treatment on the improvement of DBP; Sodium ferulate was better on the improvement of TC (WMD=–?0.70, 95%CI –?1.01 to –?0.39), HDL-c (WMD= 0.14, 95%CI 0.06 to 0.22) and TG of clinical DKD (WMD=–?0.96, 95%CI –?1.49 to –?0.43), but not superior to routine treatment on the improvement of TG of early DKD (WMD=–?0.12, 95%CI –?0.28 to 0.04); Sodium ferulate was better on the improvement of serum endothelin (WMD=–?18.72, 95%CI –?25.20 to –?12.23) and urinary endothelin (WMD=–?8.55, 95%CI –?10.92 to –?6.18). Only 8 studies mentioned sodium ferulate during treatment no adverse reactions or side effects, reportedly found in a study of mild and transient headache and a study of fatigue and dizziness. We have not seen the serious adverse events. Conclusions Current evidence demonstrates that Sodium ferulate has certain effect and relatively safe in treating patients with diabetic kidney disease.Due to the heterogeneity and the high risk of bias in included studies,the evidence is insufficient to determine the effect of sodium ferulate.Further large-scale trials are required to define the role of sodium ferulate in the treatment of DKD.
ObjectiveTo systematically review the independent physical risk factors associated with diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus.
MethodsWe searched MEDLINE, EMbase, CBM, CNKI and VIP for all studies about the independent physical risk factors associated with diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus up to December 2012. Two reviewers independently screened studies according to the inclusion and exclusion criteria, extracted data, and assessed the methodological quality of included studies. Then meta-analysis was conducted using RevMan 5.2 software.
ResultsA total of 11 studies involving 12 957 patients with type 2 diabetes were included. Of these 11 studies, 9 were cross-sectional studies, two were cohort studies, and one was case-control study. The results showed that:the main physical factors associated with DKD were:duration of diabetes (OR=1.11, 95%CI 1.05 to 1.18), waist circumference (OR=1.02, 95%CI 1.00 to 1.04), fasting glucose (OR=1.11, 95%CI 1.07 to 1.16), glycosylated hemoglobin (OR=1.20, 95%CI 1.06 to 1.36), systolic blood pressure (OR=1.03, 95%CI 1.02 to 1.05), diastolic blood pressure (OR=2.41, 95%CI 1.15 to 4.64), triglycerides (OR=1.24, 95%CI 1.02 to 1.51), high-density lipoprotein (OR=0.558, 95%CI 0.369 to 0.844), blood uric acid (OR=1.005, 95%CI 1.002 to 1.009), blood urea nitrogen (OR=1.58, 95%CI 1.37 to 1.82), past history of kidney disease (OR=3.26, 95%CI 1.20 to 8.87) and family history of kidney disease (OR=1.83, 95%CI 1.29 to 2.60).
ConclusionCurrent evidence shows that multiple physical factors were associated with the development of type 2 diabetic kidney disease. However, due to the limited quantity and quality of the included studies, more high quality studies are needed to verify the conclusion.
Diabetic kidney disease (DKD) is a major complication of diabetes mellitus. One third of patients with advanced diabetes mellitus can develop to uremia, which seriously endangers people’s health. In recent years, with the improvement of people’s living standards and the increasing incidence of diabetes, it has become the main cause of end stage renal disease in China. Over the past two decades, the understanding of diagnosis and treatment of DKD has been improved, such as putting forward the new concept of normoalbuminuric DKD and developing a variety of new anti-diabetic drugs. However, at present, the basic strategies of DKD treatment are still lifestyle modification, glucose control, blood pressure control and lipid control.
The incidence of chronic kidney disease is increasing worldwide, which greatly increases the risk of end-stage renal disease. It is particularly important to find out the risk factors for the development and progression of chronic kidney disease. Whether gender is a risk factor for the progression of kidney disease remains controversial with inconsistent results in human cohort studies with diabetic or non-diabetic kidney disease. In most of the studies, women seem to exhibit certain gender advantages. Sex hormones, renal hemodynamics and lifestyle differences may play an important role. The underlying mechanism of gender affecting the progression of kidney disease deserves further exploration. This article reviews the gender differences and possible mechanisms in diabetic and non-diabetic chronic kidney disease, in order to provide reference for future research.
Objective To assess the efficacy of Tongxinluo for diabetic kidney disease. Methods we conducted a systematic review of randomized controlled trials (RCTs) in which Tongxinluo was used to treat diabetic kidney disease. And we screened relevant studies according to predefined inclusion and exclusion criteria, evaluated the quality of the included studies, and performed meta-analyses by using The Cochrane Collaboration’s Revman 5.0 software. Results A total of 11 RCTs were enrolled in the review. The results of meta-analysis showed that Tongxinluo was better on attenuating 24 hour urinary protein,BUN and UAER; Tongxinluo was not superior to no treatment on the improvement of Scr and Ccr; Tongxinluo was better than no treatment on the Regulation of blood lipids, such as TC, TG, LDL-C. However, Tongxinluo might have similar effects on the improvement of HDL-C; Tongxinluo was better than no treatment on the improvement of FBG, but xuezhikang was not superior to no treatment on the improvement of P2BG and HbA1c. Tongxinluo was better than no treatment in decreasing plasma endothelin (ET). No significant adverse effects or Allergic reactions were reported. Conclusion The evidence currently available shows that Tongxinluo has some effect and is relatively safe in treating patients with diabetic kidney disease.Due to a high risk of selection bias and detection bias in the included studies, the evidence is insufficient to determine the effect of Tongxinluo. Further large-scale trials are required to define the role of xuezhikang in the treatment of DKD.
Objective To assess the effectiveness of xuezhikang for treating diabetic kidney disease. Methods We searched the Cochrane Central Register of Controlled Trials (Issue 3, 2008), MEDLINE (1980 to September 2008), EMbase (1980 to September 2008), CBMdisc (1990 to September 2008), and CNKI (1994 to September 2008). We also hand searched relevant journals and conference proceedings. Randomized controlled trials (RCTs) in which xuezhikang was used to treat diabetic kidney disease were collected. Then we screened the retrieved studies according to predefined inclusion and exclusion criteria, evaluated the quality of included studies, and performed metaanalyses by using The Cochrane Collaboration’s RevMan 4.2 software. Results Nine RCTs were included. Meta-analyses showed that xuezhikang was superior to routine treatment in decreasing 24-hour urinary protein (WMD –0.87, 95%CI –1.34 to –0.41), microalbuminuria (WMD –115.39, 95%CI –127.63 to –103.15), and urinary albumin excretion rate (WMD – 65.46, 95%CI –68.87 to –62.12); but xuezhikang had similar effects in reducing serum creatinine compared with routine treatment (WMD –5.42, 95%CI –11.06 to 0.21). Moreover, xuezhikang was more effective in regulating blood lipids, including TC (WMD –1.71, 95%CI –2.39 to –1.03), TG (WMD –0.96, 95%CI –1.46 to –0.46), LDL-C (WMD –1.01, 95%CI –1.64 to –0.38), and HDL-C (WMD 0.22, 95%CI 0.09 to 0.36). Xuezhikang was not superior to routine treatment in improving fasting blood sugar (WMD -0.01, 95%CI -0.49 to 0.47), but was more effective in improving 2 h-BS (WMD –1.10, 95%CI –1.35 to –0.85) and HbA1c (WMD –0.41, 95%CI –0.56 to –0.27). No significant adverse effects or allergic reactions were reported. Conclusions The evidence currently available shows that xuezhikang may decrease 24-hour urinary protein, microalbuminuria, serum creatinine, regulate blood lipids, and adjust blood glucose. Due to a high risk of selection bias and detection bias in the included studies, the evidence is insufficient to determine the effect of xuezhikang. Further large-scale trials are required to define the role of xuezhikang in the treatment of diabetic kidney disease.
ObjectivesTo investigate the predictive value for diabetic kidney disease (DKD) by utilizing deep learning to automatically segment fundus panoramic images and constructing multidimensional radiomics models integrated with clinical parameters. MethodsA diagnostic trial study. From December 2022 to March 2024, 353 patients with type 2 diabetes mellitus who were admitted for the first time to Department of Endocrinology of Yangzhou University Affiliated Jiangdu People's Hospital were included in the study. Among them, 114 cases had diabetic kidney disease (DKD), and 239 cases were non-DKD patients. All patients underwent non-mydriatic color fundus photography of both eyes, capturing 45° field of view fundus images centered on the macula (panoramic fundus images). A pre-trained U-Net model was used to automatically segment regions of interest (ROI) in the fundus panoramic images, and batch processing was performed to segment ROI for all images. Radiomics features were extracted separately from the ROI of both left and right fundus panoramic images. Feature-level fusion (feature concatenation) was applied to merge the radiomics features from both eyes, resulting in a fused feature set. Features from the left eye, right eye, and the fused set underwent feature selection using t-tests, Pearson correlation analysis, and LASSO regression to identify the optimal features for model building. Finally, the dataset was partitioned into a training set and an independent validation set at a 7:3 ratio. The training set underwent 5-fold cross-validation for hyperparameter tuning, ultimately yielding the optimal algorithm model classifier, separate radiomics models were built for the left eye, right eye, and the feature-level fusion set. Additionally, a decision-level fusion model (ensemble voting) was constructed by combining the outputs (results) of the left and right eye models. A clinical parameter model was also built based on multivariate analysis results. The area under the receiver operating characteristic curve (AUC) was used as the primary quantitative evaluation metric. DeLong's test compared AUC differences between models. The net reclassification index (NRI) and decision curve analysis (DCA) were employed to assess the superiority between different models. ResultsThe results of the ROC analysis showed that in the training and validation sets, the AUC values for the clinical model, left-eye radiomics model, right-eye radiomics model, feature-level fusion model, and decision-level fusion model were 0.826, 0.847, 0.883, 0.890, 0.907 and 0.588, 0.646, 0.642, 0.657, 0.689, respectively. The results of the DeLong test showed that in the training set, the AUC of the decision-level fusion model was significantly higher than that of the clinical model and the left-eye model (P=0.010,＜0.001). The AUC of the feature-level fusion model was significantly higher than that of the left-eye model (P=0.020). However, in the validation set, no statistically significant differences in AUC were observed among the models (P＞0.05). The results of the NRI Analysis showed that compared to the clinical model, the NRI values for all four radiomics models were positive in both training and validation sets, indicating superior DKD prediction performance by the radiomics models. Compared to the decision-level fusion model, the NRI values for the left-eye, right-eye, and feature-level fusion models were negative in both sets, suggesting that the decision-level fusion model had the best performance. The results of the DCA analysis showed that in both training and validation sets, the decision-level fusion model provided greater net clinical benefit across a range of threshold probabilities compared to the other four models. ConclusionThe radiomics model based on automatically segmented panoramic fundus images can predict the risk of DKD occurrence, with the integrated model of both eyes demonstrating higher predictive performance.
