Introduction: Dysbiosis of gut microbiota impairs the homeostasis of immune and metabolic systems. Although previous studies have revealed the correlation between gut microbiota and various diseases, the function between gut microbiota and diabetic nephropathy (DN) has not been discovered distinctly. In this study, we tried to investigate the profile and function of gut microbiota in DN. Methods: A total of 100 people were enrolled in this study. Twenty were healthy people, 20 were diabetes patients, and 60 were DN patients. The DN patients were divided into three stages including stage III, IV, and V. We conducted taxonomic analyses in different groups. The distributions of phyla, classes, orders, families, and genera in different groups and samples were investigated. We also evaluated the correlations between clinical parameters and gut microbiota in 60 DN patients. Results: The gut microbiota in the healthy group, diabetes group, and DN group had 1764 operational taxonomic units (OTUs) in total. The healthy group had 1034 OTUs, the diabetes group had 899 OTUs, and the DN group had 1602 OTUs. The diversity of gut microbiota in the stage III DN group was smaller than that in the other groups. 24-h urinary protein was positively correlated with Alistipes and Subdoligranulum, cholesterol was positively correlated with Bacteroides and Lachnoclostridium, and estimated glomerular filtration rate was negatively correlated with Ruminococcus torques group. Discussion: The gut microbiota might play an important role in the development and pathogenesis of DN. A change in gut microbiota diversity is correlated with disease progression. Some kinds of gut microbiota including Alistipes, Bacteroides, Subdoligranulum, Lachnoclostridium, and Ruminococcus torques group might be detrimental factors in DN.

Introduction: Qing-Re-Xiao-Zheng-Yi-Qi Formula is an effective prescription in diabetic kidney disease treatment, we have confirmed the efficacy of Qing-Re-Xiao-Zheng therapy in diabetic kidney disease through clinical trials. In this study, we investigated the mechanisms of Qing-Re-Xiao-Zheng-Yi-Qi Formula in the treatment of diabetic kidney disease.Methods: We used Vanquish UHPLCTM to analyze the chemical profiling of Qing-Re-Xiao-Zheng-Yi-Qi Formula freeze-dried powder. We constructed diabetic kidney disease rat models induced by unilateral nephrectomy and high-dose streptozocin injection. We examined blood urea nitrogen, serum creatinine, serum glucose, total cholesterol, triglyceride, serum total protein, albumin, alanine aminotransferase, aspartate aminotransferase and 24 h urinary total protein in diabetic kidney disease rats. The renal pathological changes were observed by HE, Masson, PAS stanning and transmission electron microscopy. The levels of fibrosis-related proteins and mitophagy-related proteins were detected by western blot analysis. We also conducted an immunofluorescence co-localization analysis on podocytes to further investigate the effect of Qing-Re-Xiao-Zheng-Yi-Qi Formula treatment on mitophagy.Results: A total of 27 constituents in Qing-Re-Xiao-Zheng-Yi-Qi Formula were tentatively identified. We found PINK1/Parkin-mediated mitophagy was inhibited in diabetic kidney disease. Qing-Re-Xiao-Zheng-Yi-Qi Formula treatment could raise body weight and reduce renal index, reduce proteinuria, improve glycolipid metabolic disorders, ameliorate renal fibrosis, and reduce the expression of Col Ⅳ and TGF-β1 in diabetic kidney disease rats. Qing-Re-Xiao-Zheng-Yi-Qi Formula treatment could also increase the expression of nephrin, activate mitophagy and protect podocytes in diabetic kidney disease rats and high glucose cultured podocytes.Conclusion: PINK1/Parkin-mediated mitophagy was inhibited in diabetic kidney disease, and Qing-Re-Xiao-Zheng-Yi-Qi Formula treatment could not only ameliorate pathological damage, but also promote mitophagy to protect podocytes in diabetic kidney disease.

Background Researchers have proved that simple renal cysts (SRCs) might be correlated with renal dysfunction, but it is still controversial. Thus, we conducted clinical research study with large sample size and long-term follow-up to clarify the relationship between SRCs and renal dysfunction. Methods A total of 571 SRCs patients in outpatients of nephrology department were included, we investigated the clinical characteristics of growth SRCs compared with non-growth SRCs, evaluated the incidence of renal dysfunction in SRCs and explored the risk factors of renal dysfunction in growth SRCs. Results The mean baseline age was 51.31 ± 14.37 years in the whole cohort, ranging from 19 to 79 years, and 57.6% of them were male. The median follow-up duration was 3 years, ranging from 1 to 10 years. In addition, the final maximum diameter increased 1 mm (2.74%) per year. Patients in growth SRCs group tented to have higher percentage of hypertension, hematuria, large cyst and multiple cysts compared with non-growth SRCs group. The prevalence of renal dysfunction was 15.6% after the follow-up, and the prevalence of renal dysfunction was about 10 times higher in growth SRCs group than non-growth SRCs group (23.3% vs. 2.4%). Renal dysfunction was significantly associated with age, female, total cholesterol, diastolic blood pressure, final maximum diameter and yearly change in maximum diameter in growth SRCs. Conclusions SRCs were closely related to the decline of renal function, we recommend close follow-up for growth SRCs.

Cardiovascular disease (CVD) is one of the most common complications of end stage renal disease (ESRD) patients receiving haemodialysis (HD). A better understanding of the relationship between CVD and HD is important to improve life expectancy and reduce the mortality risk of ESRD patients receiving HD. Heart rate variability (HRV), also known as variations in heart rate, is a useful method to measure the autonomic nervous system dys-regulation with 24-h electrocardiogram (ECG). The variables of heart rhythm complexity could be acquired with multi-scale entropy analysis (MSE). We enrolled 31 ESRD patients receiving HD in this study. MSE was used as non-linear heart rate variability (HRV) in our study. MSE area was negative correlated with age. MSE area 10-20 was positive correlated with Kt/V, albumin (ALB) and hemoglobin (Hb). Furthermore, we found that the MSE area before HD was higher than the MSE area after HD. We also found that increased age was a deleterious factor of HRV in ESRD patients receiving HD. Kt/V, ALB, and Hb were beneficial factors of HRV in ESRD patients receiving HD. HD seemed to be a harmful factor of HRV in ESRD patients.