Background: The risk of cardiovascular disease (CVD) in diabetes mellitus (DM) patients is two- to three-fold higher than in the general population. We designed a 10-year cohort trial in T2DM patients to explore the performance of QRESEARCH risk estimator version 3 (QRISK3) as a CVD risk assessment tool and compared to Framingham Risk Score (FRS).Method: This is a single-center analysis of prospective data collected from 566 newly-diagnosed patients with type 2 DM (T2DM). The risk scores were compared to CVD development in patients with and without CVD. The risk variables of CVD were identified using univariate analysis and multivariate cox regression analysis. The number of patients classified as low risk (20%) for two tools were identified and compared, as well as their sensitivity, specificity, positive and negative predictive values, and consistency (C) statistics analysis.Results: Among the 566 individuals identified in our cohort, there were 138 (24.4%) CVD episodes. QRISK3 classified most CVD patients as high risk, with 91 (65.9%) patients. QRISK3 had a high sensitivity of 91.3% on a 10% cut-off dichotomy, but a higher specificity of 90.7% on a 20% cut-off dichotomy. With a 10% cut-off dichotomy, FRS had a higher specificity of 89.1%, but a higher sensitivity of 80.1% on a 20% cut-off dichotomy. Regardless of the cut-off dichotomy approach, the C-statistics of QRISK3 were higher than those of FRS.Conclusion: QRISK3 comprehensively and accurately predicted the risk of CVD events in T2DM patients, superior to FRS. In the future, we need to conduct a large-scale T2DM cohort study to verify further the ability of QRISK3 to predict CVD events.

Background: Diabetic kidney disease (DKD), a common microvascular complication of diabetes mellitus (DM), is always asymptomatic until it develops to the advanced stage. Thus, we aim to develop a nomogram prediction model for progression to DKD in newly diagnosed type 2 diabetes mellitus (T2DM). Methods: This was a single-center analysis of prospective data collected from 521 newly diagnosed patients with T2DM. All related clinical records were incorporated, including the triglyceride-glucose index (TyG index). The least absolute shrinkage and selection operator (LASSO) was used to build a prediction model. In addition, discrimination, calibration, and clinical practicality of the nomogram were evaluated. Results: In this study, 156 participants were incorporated as the validation set, while the remaining 365 were incorporated into the training set. The predictive factors included in the individualized nomogram prediction model included 5 variables. The area under the curve (AUC) for the prediction model was 0.826 (95% CI 0.775 to 0.876), indicating excellent discrimination performance. The model performed exceptionally well in terms of predictive accuracy and clinical applicability, according to calibration curves and decision curve analysis. Conclusion: The predictive nomogram for the risk of DKD in newly diagnosed T2DM patients had outstanding discrimination and calibration, which could help in clinical practice.

Purpose: Diabetic kidney disease (DKD) is a major contributor to chronic kidney disease worldwide. Bile acids (BAs) are increasingly recognized as key regulators of glucose metabolism and kidney function. This study aimed to investigate the role of BA metabolism in the progression of DKD. Methods: Plasma BA profiles were measured in healthy controls (HC), patients with type 2 diabetes mellitus (T2DM), and patients with DKD using ultra-high-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). After identifying potential BA biomarkers in the clinical cohort, in vivo validation was conducted using dehydrolithocholic acid (DHLCA) intervention in DKD mouse model. Kidney injury markers, as well as the expression of Takeda G protein-coupled receptor 5 (TGR5) and farnesoid X receptor (FXR), were evaluated. In addition, gut microbiota (GM) composition was analyzed via metagenomic sequencing following DHLCA treatment. Results: The plasma DHLCA levels were significantly lower in DKD with macroalbuminuria group compared to T2DM group and DKD with microalbuminuria group (P < 0.01). Partial Spearman correlation analysis adjusted for age and diabetes duration showed that DHLCA levels were negatively correlated with urine albumin (ρ = – 0.347; 95% CI, – 0.531 to – 0.135; q = 0.008) and urine albumin-to-creatinine ratio (UACR) (ρ = – 0.332; 95% CI, – 0.499 to – 0.155; q = 0.010). In vivo, DHLCA administration significantly reduced UACR and fasting blood glucose (FBG) levels (P < 0.01), and improved liver function (ALT, P < 0.05) in DKD mice. DHLCA treatment attenuated renal tubular injury, restored TGR5 and FXR expression in kidney tissue, and decreased levels of kidney injury molecule-1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL). Metagenomic analysis revealed an enrichment of Lachnospiraceae bacterium following DHLCA treatment. Conclusion: DHLCA may represent a promising therapeutic candidate for DKD by targeting the TGR5/FXR signaling pathway and GM remodeling. Its metabolic and kidney benefits, along with an improved hepatic profile and absence of hepatotoxicity, support further translational investigation.

Aim: The aim of this study was to develop a predictive model for the progression of diabetic kidney disease (DKD) to end-stage renal disease (ESRD) and to evaluate the effectiveness of renal pathology and the kidney failure risk equation (KFRE) in this context. Methods: The study comprised two parts. The first part involved 555 patients with clinically diagnosed DKD, while the second part focused on 85 patients with biopsy-proven DKD. Cox regression analysis and competing risk regression were employed to identify independent predictors. Time-dependent receiver operating characteristic (ROC) was used to evaluate prediction performance, and the area under the curve (AUC) was calculated to assess the model’s accuracy. Results: The Cox regression model developed for the 555 patients clinically diagnosed with DKD identified 5 predictors (body mass index (BMI), estimated glomerular filtration rate (eGFR), 24-hour urinary total protein (UTP), systemic immune-inflammatory index (SII), and controlling nutritional status (CONUT), whereas the Competing risks model included 4 predictors (BMI, eGFR, UTP, CONUT). Among 85 patients with biopsy-proven diabetic DKD, the combined prognostic model integrating KFRE, interstitial fibrosis and tubular atrophy (IFTA), SII and BMI demonstrated enhanced predictive ability at 5 years. The developed models offer improved accuracy over existing methods by incorporating renal pathology and novel inflammatory indices, making them more applicable in clinical settings. Conclusion: The predictive model proved to be effective in assessing the progression of DKD to ESRD. Additionally, the combined model of KFRE, IFTA, SII, and BMI demonstrates high predictive performance. Future studies should validate these models in larger cohorts and explore their integration into routine clinical practice to enhance personalized risk assessment and management.

Although the patient survival rate for many malignancies has been improved with immune checkpoint inhibitors (ICIs), some patients experience various immune-related adverse events (irAEs). IrAEs impact several organ systems, including the kidney. With anti-programmed cell death protein 1 (PD-1) therapy (pembrolizumab), kidney-related adverse events occur relatively rarely compared with other irAEs. However, the occurrence of AKI usually leads to anti-PD-1 therapy interruption or discontinuation. Therefore, there is an urgent need to clarify the mechanisms of renal irAEs (R-irAEs) to facilitate early management. This study aimed to analyse the characteristics of peripheral blood mononuclear cells (PBMCs) in R-irAEs. PBMCs were collected from three patients who developed R-irAEs after anti-PD-1 therapy and three patients who did not. The PBMCs were subjected to scRNA-seq to identify cell clusters and differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses were performed to investigate the most active biological processes in immune cells. Fifteen cell clusters were identified across the two groups. FOS, RPS26, and JUN were the top three upregulated genes in CD4+ T cells. The DEGs in CD4+ T cells were enriched in Th17 differentiation, Th1 and Th2 cell differentiation, NF-kappa B, Nod-like receptor, TNF, IL-17, apoptosis, and NK cell-mediated cytotoxicity signaling pathways. RPS26, TRBV25-1, and JUN were the top three upregulated genes in CD8+ T cells. The DEGs in CD8+ T cells were enriched in Th17 cell differentiation, antigen processing and presentation, natural killer cell-mediated cytotoxicity, the intestinal immune network for IgA production, the T-cell receptor signalling pathway, Th1 and Th2 cell differentiation, the phagosome, and cell adhesion molecules. In conclusion, R-irAEs are associated with immune cell dysfunction. DEGs and their enriched pathways identified in CD4+ T cells and CD8+ T cells play important roles in the development of renal irAEs related to anti-PD-1 therapy. These findings offer fresh perspectives on the pathogenesis of renal damage caused by anti-PD-1 therapy.