Remnant cholesterol (RC) is closely related to metabolic diseases. Our study aims to explore the relationship between RC and hyperuricemia. This cross-sectional study included 14 568 adults aged 20 years or older from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2018 in the United States. RC is calculated by subtracting high-density lipoprotein cholesterol (HDL-c) and low-density lipoprotein cholesterol (LDL-c) from total cholesterol (TC). Hyperuricemia is defined by serum uric acid (SUA) levels≥7 mg/dl in men and≥6 mg/dl in women. The independent association between RC and hyperuricemia was evaluated. As the quartile range of RC levels increases, the prevalence of hyperuricemia also rises (7.84% vs. 13.71% vs. 18.61% vs. 26.24%, p<0.001). After adjusting for confounding factors, the fourth quartile of RC was associated with an increased risk of hyperuricemia compared with the first quartile (OR=2.942, 95% CI 2.473–3.502, p<0.001). Receiver Operating Characteristic (ROC) analysis shows that RC outperforms other single lipid indices in hyperuricemia. Further Restricted Cubic Splines (RCS) analysis suggests a nonlinear relationship between RC levels and hyperuricemia. Elevated RC levels were found to be linked to hyperuricemia. Further studies on RC hold promise for both preventing and addressing hyperuricemia.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) have demonstrated significant cardiovascular and renal benefits beyond glycemic control, yet their integrated mechanisms remain incompletely understood. Emerging evidence highlights the gut-kidney-heart axis as a pivotal pathological network, wherein gut dysbiosis, toxic metabolite accumulation, intestinal barrier disruption, and systemic inflammation synergistically drive cardiorenal injury. This review systematically elucidates how SGLT2i modulate this axis through multi-level interventions: reshaping gut microbiota composition, enriching short-chain fatty acid-producing bacteria, suppressing trimethylamine and other toxin-generating microbes, restoring tight junction integrity, and regulating bile acid metabolism. These upstream effects reduce systemic inflammatory and metabolic stress, interrupt kidney-derived toxin amplification, and mitigate myocardial remodeling. Unlike previous reviews focusing on single-organ pathways, this work integrates microecological regulation, metabolite reprogramming, and cross-organ protection into a unified “three-axis convergence to the heart” framework. We also highlight potential species-specific microbiota regulatory profiles among different SGLT2i and propose future directions, including fecal microbiota transplantation and microbiota-targeted co-therapies, to clarify causal relationships and optimize therapeutic strategies. By positioning the gut as a modifiable upstream driver, this framework provides novel mechanistic insight and translational potential for expanding SGLT2i applications in metabolic cardiovascular disease, including in non-diabetic populations.

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.

Objective: Type 2 diabetes mellitus (T2DM) is a metabolic disease characterized by insulin resistance and progressively impaired insulin secretion resulting in dynamic fluctuations in glucose levels.High blood urea nitrogen (BUN) levels have been linked to decreased insulin sensitivity, suppressed insulin synthesis and increased risk of incident diabetes mellitus in humans as well as insulin use in patients with T2DM.This study characterize the association between BUN levels and short-term and long-term glycemic variability(GV) in the elderly patients with T2DM who were hospitalized. Methods: A total of 927 elderly patients with T2DM were included in the study. The short-term GV was quantified using parameters such as standard deviation (SD), coefficient of variation (CV), time in range (TIR), and mean amplitude of glycemic excursions (MAGE), based on multi-point fingertip blood glucose monitoring. The long-term GV was quantified using parameters such as SD, CV, variation independent of the mean (VIM), and average successive variability (ARV), based on fasting blood glucose(FPG). The relationship between BUN levels and short-term and long-term GV in elderly T2DM who were hospitalized was explored using methods such as Spearman correlation coefficient, linear regression analysis, logistic regression analysis, and interaction tests. Results: In elderly patients with T2DM were hospitalized, there is a significant correlation between BUN levels and both short-term and long-term GV. BUN is negatively correlated with the GV parameter TIR (r=− 0.12, P=0.000), and positively correlated with SD (r=0.12, P=0.000), CV (r=0.07, P=0.026), MAGE (r=0.11, P=0.001), FPG-SD (r=0.08, P=0.013), and FPG-CV (r=0.08, P=0.014).Furthermore, the association remains consistent across different age, gender, BMI, and haemoglobin A1c (HbA1c) subgroups (P interaction > 0.05). Conclusion: In elderly patients with T2DM were hospitalized, BUN levels were positively associated with GV.Therefore, monitoring BUN levels were beneficial in assessing the degree of GV.