Association between Circulating Levels of 25-Hydroxyvitamin D3 and Matrix Metalloproteinase-10 (MMP-10) in Patients with Type 2 Diabetes

Unraveling DDIT4 in the VDR-mTOR pathway: a novel target for drug discovery in diabetic kidney disease

Introduction

Diabetic kidney disease (DKD) necessitates innovative therapeutic strategies. This study delves into the role of DNA damage-inducing transcription factor 4 (DDIT4) within the VDR-mTOR pathway, aiming to identify a novel target for DKD drug discovery.

Methods

Transcriptome data from the Gene Expression Omnibus Database were analyzed to assess the expression of mTOR and VDR expression in human renal tissues. Clinical samples from DKD patients and minimal change disease (MCD) controls were examined, and a DKD animal model using 20-week-old db/db mice was established. DDIT4 plasmid transfection was employed to modulate the VDR-mTOR pathway, with its components evaluated using immunohistochemistry, real-time quantitative PCR (qRT-PCR), Western blotting, and enzyme-linked immunosorbent assay (ELISA).

Results

Changes in the expression of the VDR-mTOR pathway were observed in both DKD patients and the animal model. Overexpression of DDIT4 increased VDR expression and decreased levels of mTOR, p70s6k, and 4E-BP1. Furthermore, DDIT4 treatment regulated autophagy by upregulating LC3I expression and downregulating LC3II expression. Notably, DDIT4 alleviated oxidative stress by reducing the levels of lipid peroxidation product MDA, while simultaneously increasing the levels of superoxide dismutase (SOD) and glutathione (GSH), underscoring the role of DDIT4 in the pathological process of DKD and its potential as a therapeutic target.

Conclusion

Unraveling DDIT4's involvement in the VDR-mTOR pathway provides insights for innovative DKD drug discovery, emphasizing its potential as a therapeutic target for future interventions.

Construction of a Nomogram-Based Prediction Model for the Risk of Diabetic Kidney Disease in T2DM

Introduction

To investigate the predictors of diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients and establish a nomogram model for predicting the risk of DKD.

Methods

The clinical data of T2DM patients, admitted to the Endocrinology Department of Chengde Central Hospital from October 2019 to September 2020 and divided into a case group or a control group based on whether they had DKD, were collected. The predictive factors of DKD were screened by univariate and multivariate analysis, and a nomogram prediction model was constructed for the risk of DKD in T2DM. Bootstrapping was used for model validation, receiver operating characteristic (ROC) curve and GiViTI calibration curve were used for evaluating the discrimination and calibration of prediction model, and decision analysis curve (DCA) was used for evaluating the practicality of model.

Results

Predictors for DKD are diabetic retinopathy (DR), hypertension, history of gout, smoking history, using insulin, elevation of body mass index (BMI), triglyceride (TG), cystatin C (Cys-C), and reduction of 25 (OH) D. The nomogram prediction model based on the above nine predictors had good representativeness (Bootstrap method: precision: 0.866, Kappa: 0.334), differentiation [the area under curve (AUC) value: 0.868], and accuracy (GiViTI-corrected curved bands, P = 0.836); the DAC curve analysis showed that the prediction model, whose threshold probability was in the range of 0.10 to 0.70, had clinical practical value.

Conclusion

The risk of DKD in T2DM could be predicted accurately by DR, hypertension, history of gout, smoking history, using insulin, elevation of BMI, TG, Cys-C, and reduction of 25 (OH) D.