diabetes mellitus (dM) is a metabolic disorder, which if not managed properly, can lead to serious health problems over time and impose significant financial burden on the patient, their family and society as a whole. The study of this disease and the underlying biological mechanism is gaining momentum. Multiple pieces of conclusive evidence show that ceramides are involved in the occurrence and development of diabetes. The present review focuses on the function of ceramides, a type of sphingolipid signaling molecule, to provide a brief description of ceramides and their metabolism, discuss the significant roles of ceramides in the healthy skin barrier, and speculate on the potential involvement of ceramides in the pathogenesis and development of diabetic foot ulcers (dFUs). Understanding these aspects of this disease more thoroughly is crucial to establish how ceramides contribute to the etiology of diabetic foot infections and identify possible therapeutic targets for the treatment of dFUs. Contents1. Introduction 2. Biosynthesis and degradation of ceramides 3. Ceramides in the skin 4. ceramides and dFUs 5. ceramides and vessels 6. ceramides and diabetes 7. ceramides and atherosclerosis 8. Mechanism of ceramides in diabetes 9. Mechanism of ceramides in atherosclerosis 10. Shared mechanisms between diabetic complications and dFUs 11. conclusions

The research explores the relationship between the triglyceride-glucose index (TyG index) and the macroangiopathy risk in single-center hospitalized type 2 diabetes mellitus (T2DM) patients and develops a risk prediction nomogram model. Patients and Methods: A total of 858 patients with T2DM were studied retrospectively. Lasso regression was used to eliminate unimportant factors, and multivariate logistic regression analysis was used to investigate the association between the TyG index and macrovascular disease in T2DM. A nomogram model was constructed to predict macrovascular disease in T2DM and tested using the bootstrap technique, and the efficacy of the nomogram model was investigated using ROC curves. The multivariate Cox proportional hazards model estimated the association between the TyG index and all-cause mortality. Results: TyG index, high-density lipoprotein, red blood cell count, hypertension, history of taking ACEI/ARB drugs, and aortic calcification were closely related to macrovascular complications. In Cox proportional hazard model, the HRs of TyG index were 1.89 (95% confidence interval (CI) 1.29-2.76, p < 0.001) after adjusting for covariates. The risk of all-cause mortality in T2DM with macrovascular complications was significantly higher than in diabetic patients without vascular disease. In the ROC curve analysis, the cut-off value of the TyG index for macrovascular complications of T2DM was 9.31 (AUC: 0.702, 95% CI 0.67-0.74, p < 0.001). Conclusion: TyG index predicts future macrovascular disease in diabetic patients independently of known cardiovascular risk factors, suggesting that TyG index may be a useful marker for prognosis in diabetic patients.

Background: atherosclerosis is a multifaceted disease characterized by the formation and accumulation of plaques that fix to the arteries and causes some cardiovascular disease and vascular embolism. A range of diagnostic techniques, including selective coronary angiography, stress tests, CT, and nuclear scans allow assessment of cardiovascular disease risk and treatment targets. However, there is not a very simple blood biochemical index or biological target for the diagnosis of atherosclerosis at present. So it would be interesting to find a blood biochemical marker for atherosclerosis.Methods: Three datasets from Gene Expression Omnibus (GEO) database were analyzed to obtain differentially expressed genes (DEG) and the results were integrated using Robustrankaggreg algorithm. The genes considered more important by Robustrankaggreg algorithm were put into their own data set and the data set system with cell classification information for verification.Results: 21 possible genes were screened out. Interestingly, we found a good correlation between RPS4Y1, EIF1AY and XIST. In addition, we know the general expression of these genes in different cell types and whole blood cellsConclusions: In this study, we identified BTNL8 and BLNK as having good clinical significance. These results will contribute to the study of the underlying genes involved in the progression of atherosclerosis and provide insights for the discovery of new diagnostic and evaluation methods.

Background: atherosclerosis is a multifaceted disease characterized by the formation and accumulation of plaques that fix to the arteries and causes some cardiovascular disease and vascular embolism. A range of diagnostic techniques, including selective coronary angiography, stress tests, CT, and nuclear scans allow assessment of cardiovascular disease risk and treatment targets. However, there is not a very simple blood biochemical index or biological target for the diagnosis of atherosclerosis at present. So it would be interesting to find a blood biochemical marker for atherosclerosis.Methods: Three datasets from Gene Expression Omnibus (GEO) database were analyzed to obtain differentially expressed genes (DEG) and the results were integrated using Robustrankaggreg algorithm. The genes considered more important by Robustrankaggreg algorithm were put into their own data set and the data set system with cell classification information for verification.Results: 21 possible genes were screened out. Interestingly, we found a good correlation between RPS4Y1, EIF1AY and XIST. In addition, we know the general expression of these genes in different cell types and whole blood cellsConclusions: In this study, we identified BTNL8 and BLNK as having good clinical significance. These results will contribute to the study of the underlying genes involved in the progression of atherosclerosis and provide insights for the discovery of new diagnostic and evaluation methods.