Background. Cellular senescence is thought to play a significant role in the onset and development of diabetic nephropathy. The goal of this study was to explore potential biomarkers associated with diabetic glomerulopathy from the perspective of senescence. Methods. Datasets about human glomerular biopsy samples related to diabetic nephropathy were systematically obtained from the Gene Expression Omnibus database. Hub senescence‐associated genes were investigated by differential gene analysis and Least Absolute Shrinkage and Selection Operator analysis. Cluster analysis was employed to identify senescence molecular subtypes. A single‐cell dataset was used to validate the above findings and further evaluate the senescence environment. The relationship between these genes and the glomerular filtration rate was explored based on the Nephroseq database. These gene expressions have also been explored in various kidney diseases. Results. Twelve representative senescence‐associated genes (VEGFA, IQGAP2, JUN, PLAT, ETS2, ANG, MMP14, VEGFC, SERPINE2, CXCR2, PTGES, and EGF) were finally identified. Biological changes in immune inflammatory response, cell cycle regulation, metabolic regulation, and immune microenvironment have been observed across different molecular subtypes. The above results were also validated based on single‐cell analysis. Additionally, we also identified several significantly altered cell communication pathways, including COLLAGEN, PTN, LAMININ, SPP1, and VEGF. Finally, almost all these genes could well predict the occurrence of diabetic glomerulopathy based on receiver operating characteristic analysis and are associated with the glomerular filtration rate. These genes are differently expressed in various kidney diseases. Conclusion. The present study identified potential senescence‐associated biomarkers and further explored the heterogeneity of diabetic glomerulopathy that might provide new insights into the diagnosis, assessment, management, and personalized treatment of DN.

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Objective: The serum uric acid-to-high-density lipoprotein cholesterol ratio (UHR) is considered an emerging indicator of inflammatory and metabolic disorders. The aim of this research is to assess the link between UHR and the susceptibility to type 2 diabetes among elderly people in Chinese communities. Methods: A retrospective cohort study was conducted. The participants were elderly non-diabetic individuals who underwent annual health check-ups in the Kunshan community, China, from January 2018 to December 2023. A multivariable Cox proportional hazards model was used to investigate the relationship between baseline UHR levels and the incidence of type 2 diabetes in the elderly population. Results: After a median follow-up of 3.88 years, 773 type 2 diabetes events were recorded among the 7525 elderly non-diabetic individuals. After adjustment for confounders, each 1–standard deviation (SD) increase in the UHR was associated with a higher risk of type 2 diabetes (HR = 1.156, 95% CI 1.069-1.250, P < 0.001), even within the normal UHR range. The relationship between UHR and type 2 diabetes risk is more pronounced in non-obese individuals. Conclusion: In Chinese community-dwelling elderly individuals, baseline UHR levels are associated with the risk of developing type 2 diabetes. Monitoring UHR levels might help predict and assess the risk of type 2 diabetes.

Background: Our goal was to examine the causal link between blood metabolites, their ratios, and the risk of developing proliferative diabetic retinopathy (PDR) from a genetic insight.Methods: Summary‐level data about 1400 blood metabolites and their ratios, as well as PDR, were sourced from prior genome‐wide association studies (GWAS). A two‐sample univariate and multivariate Mendelian randomization (MR) approach was utilized. Additionally, metabolic pathway analysis and sensitivity analysis were also conducted.Results: After adjusting for multiple tests, four blood metabolites significantly correlated with PDR risk. Two ceramides, including glycosyl‐N‐palmitoyl‐sphingosine (d18:1/16:0) (odds ratio [OR] = 1.12, 95% confidence interval (CI): 1.06–1.17, p < 0.001, false discovery rate (FDR) = 0.005) and glycosyl‐N‐behenoyl‐sphingadienine (d18:2/22:0) (OR = 1.11, 95% CI: 1.06–1.16, p < 0.001, FDR = 0.017), were linked to increased risk. Additionally, 3‐methylcytidine (OR = 1.05, 95% CI: 1.03–1.08, p < 0.001, FDR = 0.021) also posed a risk, whereas (N(1)+N(8))‐acetylspermidine (OR = 0.91, 95% CI: 0.87–0.94, p < 0.001, FDR = 0.002) appeared protective. Multivariable MR analysis further confirmed a direct, protective effect of (N(1)+N(8))‐acetylspermidine on PDR risk (OR = 0.94, 95% CI: 0.89–1.00, p = 0.040). The sensitivity analysis results indicated that evidence for heterogeneity and pleiotropy was absent.Conclusion: These metabolites have the potential to be used as biomarkers and are promising for future research into the mechanisms and drug targets for PDR.