Prevalence of diabetic nephropathy in the diabetes mellitus population: A protocol for systematic review and meta-analysis

Application of hospital-community-home linkage management model in patients with type 2 diabetic nephropathy

OBJECTIVE

To explore the effect of the hospital-community-home (HCH) linkage management mode in patients with type 2 diabetic nephropathy (DN).

METHOD

A total of 80 patients with type 2 DN hospitalised in the Department of Nephrology of our hospital between July 2021 and June 2022 were recruited and subsequently divided into the observation group and the control group using the random number table method, with 40 patients in each group. The control group received routine health education and discharge guidance. The HCH linkage management model was implemented for the observation group based on routine care. The improvements in compliance behaviour, biochemical parameters of renal function, blood glucose level and self-management ability were compared before the intervention and at 3 and 6 months after the intervention.

RESULTS

After the intervention, the scores for compliance behaviour of the observation group were better than those of the control group, with a statistically significant difference (P < 0.05). The biochemical indicators of renal function and blood glucose level were significantly lower in the observation group compared with in the control group, with a statistically significant difference (P < 0.05). After the intervention, the observation group showed a great improvement in self-management ability and cognition of the disease, with significant differences (P < 0.05).

CONCLUSION

The HCH linkage management mode can improve the compliance behaviour of patients with type 2 DN, effectively improve the renal function and blood sugar level of patients, enhance the self-management ability and cognition of the disease and delay the development of the disease.

Associations of genetic variants contributing to gut microbiota composition in diabetic nephropathy

Introduction

The gut microbiota is strongly associated with multiple kidney diseases, and since microbial composition is heritable, we hypothesized that genetic variations controlling gut microbiota composition were associated with diabetic nephropathy susceptibility or clinical subphenotypes.

Methods

The genetic variations associated with gut microbiota were retrieved from the genome-wide association study database and analysed in our diabetic nephropathy susceptibility gene screening cohort. Candidate microorganisms with possible genetic associations were identified using the annotation of microbial quantitative trait loci. Finally, the candidate microorganisms were verified by 16S rDNA gene sequencing.

Results

There were 13 genetic variation loci associated with susceptibility to diabetic nephropathy. The TCF7L2 risk genotype was associated with a long duration of diabetes and high diastolic blood pressure, the ZCWPW2 risk genotype was associated with increased glycosylated hemoglobin, and the ZNRF3 risk genotype was associated with an increased urinary microalbumin-to-creatinine ratio. Both the ZNRF3 and SPECC1L risk genotypes were associated with the abundance of Lactococcus. 16S rDNA sequencing confirmed that there was indeed a significant difference in the Lactococcus genus between DN and DM patients.

Conclusions

In this study, we preliminarily confirmed that the gut microbiota of diabetic nephropathy patients is influenced by host genetics and provide a new basis for future accurate diagnosis and treatment.

Vascular complications of diabetes: A narrative review

Diabetes mellitus is a complex chronic metabolic disease characterized by hyperglycemia and various complications. According to the different pathophysiological mechanisms, these complications can be classified as microvascular or macrovascular complications, which have long-term negative effects on vital organs such as the eyes, kidneys, heart, and brain, and lead to increased patient mortality. Diabetes mellitus is a major global health issue, and its incidence and prevalence have increased significantly in recent years. Moreover, the incidence is expected to continue to rise as more people adopt a Western lifestyle and diet. Thus, it is essential to understand the epidemiology, pathogenesis, risk factors, and treatment of vascular complications to aid patients in managing the disease effectively. This paper provides a comprehensive review of the literature to clarify the above content. Furthermore, this paper also delves into the correlation between novel risk factors, such as long noncoding RNAs, gut microbiota, and nonalcoholic fatty liver disease, with diabetic vascular complications.

Machine-learning algorithm-based prediction of a diagnostic model based on oxidative stress-related genes involved in immune infiltration in diabetic nephropathy patients

Diabetic nephropathy (DN) is the most prevalent microvascular consequence of diabetes and has recently risen to the position of the world's second biggest cause of end-stage renal diseases. Growing studies suggest that oxidative stress (OS) responses are connected to the advancement of DN. This study aimed to developed a novel diagnostic model based on OS-related genes. The differentially expressed oxidative stress-related genes (DE-OSRGs) experiments required two human gene expression datasets, which were given by the GEO database (GSE30528 and GSE96804, respectively). The potential diagnostic genes were identified using the SVM-RFE assays and the LASSO regression model. CIBERSORT was used to determine the compositional patterns of the 22 different kinds of immune cell fraction seen in DN. These estimates were based on the combined cohorts. DN serum samples and normal samples were both subjected to RT-PCR in order to investigate the degree to which certain genes were expressed. In this study, we were able to locate 774 DE-OSRGs in DN. The three marker genes (DUSP1, PRDX6 and S100A8) were discovered via machine learning on two different machines. The high diagnostic value was validated by ROC tests, which focused on distinguishing DN samples from normal samples. The results of the CIBERSORT study suggested that DUSP1, PRDX6, and S100A8 may be associated to the alterations that occur in the immunological microenvironment of DN patients. Besides, the results of RT-PCR indicated that the expression of DUSP1, PRDX6, and S100A8 was much lower in DN serum samples compared normal serum samples. The diagnostic value of the proposed model was likewise verified in our cohort, with an area under the curve of 9.946. Overall, DUSP1, PRDX6, and S100A8 were identified to be the three diagnostic characteristic genes of DN. It's possible that combining these genes will be effective in diagnosing DN and determining the extent of immune cell infiltration.