The effects of compound centella formula on OxInflammation and silent information regulator 1 in a high-fat diet/streptozotocin-induced diabetic kidney disease rat model

Association of systemic immune-inflammation index with diabetic kidney disease in patients with type 2 diabetes: a cross-sectional study in Chinese population

Objective

Systemic immune-inflammation index (SII), a novel inflammatory marker, has been reported to be associated with diabetic kidney disease (DKD) in the U.S., however, such a close relationship with DKD in other countries, including China, has not been never determined. We aimed to explore the association between SII and DKD in Chinese population.

Methods

A total of 1922 hospitalized patients with type 2 diabetes mellitus (T2DM) included in this cross-sectional study were divided into three groups based on estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (ACR): non-DKD group, DKD stages 1-2 Alb group, and DKD-non-Alb+DKD stage 3 Alb group. The possible association of SII with DKD was investigated by correlation and multivariate logistic regression analysis, and receiver-operating characteristic (ROC) curves analysis.

Results

Moving from the non-DKD group to the DKD-non-Alb+DKD stage 3 Alb group, SII level was gradually increased (P for trend <0.01). Partial correlation analysis revealed that SII was positively associated with urinary ACR and prevalence of DKD, and negatively with eGFR (all P<0.01). Multivariate logistic regression analysis showed that SII remained independently significantly associated with the presence of DKD after adjustment for all confounding factors [(odds ratio (OR), 2.735; 95% confidence interval (CI), 1.840-4.063; P < 0.01)]. Moreover, compared with subjects in the lowest quartile of SII (Q1), the fully adjusted OR for presence of DKD was 1.060 (95% CI 0.773-1.455) in Q2, 1.167 (95% CI 0.995-1.368) in Q3, 1.266 (95% CI 1.129-1.420) in the highest quartile (Q4) (P for trend <0.01). Similar results were observed in presence of DKD stages 1-2 Alb or presence of DKD-non- Alb+DKD stage 3 Alb among SII quartiles. Last, the analysis of ROC curves revealed that the best cutoff values for SII to predict DKD, Alb DKD stages 1- 2, and DKD-non-Alb+ DKD stage 3 Alb were 609.85 (sensitivity: 48.3%; specificity: 72.8%), 601.71 (sensitivity: 43.9%; specificity: 72.3%), and 589.27 (sensitivity: 61.1%; specificity: 71.1%), respectively.

Conclusion

Higher SII is independently associated with an increased risk of the presence and severity of DKD, and SII might be a promising biomarker for DKD and its distinct phenotypes in Chinese population.

Translation Animal Models of Diabetic Kidney Disease: Biochemical and Histological Phenotypes, Advantages and Limitations

Animal models play a crucial role in studying the pathogenesis of diseases, developing new drugs, identifying disease risk markers, and improving means of prevention and treatment. However, modeling diabetic kidney disease (DKD) has posed a challenge for scientists. Although numerous models have been successfully developed, none of them can encompass all the key characteristics of human DKD. It is essential to choose the appropriate model according to the research needs, as different models develop different phenotypes and have their limitations. This paper provides a comprehensive overview of biochemical and histological phenotypes, modeling mechanisms, advantages and limitations of DKD animal models, in order to update relevant model information and provide insights and references for generating or selecting the appropriate animal models to fit different experimental needs.

The Effect of Chinese Medicinal Formulas on Biomarkers of Oxidative Stress in STZ-Induced Diabetic Kidney Disease Rats: A Meta-Analysis and Systematic Review

Background

Diabetic kidney disease (DKD), defined broadly as persistent proteinuria with low estimated glomerular filtration rate in patients with diabetes, is a main cause of end-stage renal disease. Excessive production of reactive oxygen species is an important mechanism underlying the pathogenesis of DKD and many antioxidants have been investigated as therapeutic agents. Among them, Chinese medicine antioxidative stress therapies have been widely used to combat DKD, which may offer new insights into therapeutic development of DKD. There are several discrepancies among the efficacy of Western medicine (WM) and Chinese medicinal formula (CMF) action.

Methods

We searched PubMed, Cochrane Library, the Web of Science databases, Embase, and Scopus from inception to December 2021 using relevant keywords and a comprehensive search for randomized controlled trials (RCTs) was performed. Calculating the pooled weighted mean difference (MD) and 95% CI by the method of inverse-variance with a random-effect. All the related statistical analyses were performed using Stata version 15.1 software (Stata Corporation) and Rvman version 5.3 (Nordic Cochrane Center).

Results

A total of 8 articles with the 9 groups including 106 in the model group, 105 in the CMF group, and 99 in the WM group. Pooled data from 8 studies (9 groups) showed a statistical improvement in superoxide dismutase compared with the model group [standardized MD (SMD) = 1.57; 95 CI: 1.16–1.98; P < 0.05] and the WM group (SMD = 0.56; 95 CI: 0.19–0.92; P < 0.05). For glutathione peroxidase (GSH-Px), it was significantly improved in the CMF group vs. the model group and the WM group. For malondialdehyde (MDA), it was significantly reduced in the CMF group (CMF vs. model group: SMD = −1.52; 95 CI: −1.88 −1.17; P < 0.05; CMF vs. WM group: SMD = −0.64; 95 CI: −0.95 −0.33; P < 0.05).

Conclusion

This systematic review and meta-analysis have demonstrated that the therapy of CMF had a notable curative effect on relieving oxidative stress in STZ-induced DKD rats and CMF was significantly more effective than the WM control group. For the clinical application, the results providing confidence and some theoretical reference for DKD via evaluating the efficacy of CMF to a certain extent.

Systematic Review Registration

[PROSPERO], identifier [CRD42022313737].