Previous studies have shown that visit-to-visit blood pressure variability (BPV) is associated with chronic kidney disease (CKD). However, the results have not been consistent among studies. This systematic review and meta-analysis was conducted to comprehensively assess the association between visit-to-visit BPV and the risk of CKD. MethodsMedline, Embase, and the Cochrane Library were searched from the date of inception through 1 August 2019 using the terms "blood pressure variability," "chronic kidney disease," "nephropathy," and other comparable terms. The primary outcome was the development of CKD. Two reviewers extracted the data independently. Meta-analysis was performed using a random effects model. ResultsFourteen studies were included in the systematic review and meta-analysis. The risk of CKD was significantly greater in patients with high baseline systolic blood pressure variability (SBPV) than in patients with low baseline SBPV: the standard deviation (SD) showed relative risk (RR) of 1.69 and 95% CI of 1.38-2.08, the coefficient of variation (CV) showed RR of 1.23 and 95% CI of 1.12-1.36, and variance independent of mean (VIM) showed RR of 1.40 and 95% CI of 1.15-1.71. RRs for each unit increase in visit-to-visit SBPV and risk of CKD were 1.05 (95%

Background. Clear cell renal cell carcinoma (ccRCC) is a cancer with abnormal metabolism. The purpose of this study was to investigate the effect of metabolism-related genes on the prognosis of ccRCC patients. Methods. The data of ccRCC patients were downloaded from the TCGA and the GEO databases and clustered using the nonnegative matrix factorization method. The limma software package was used to analyze differences in gene expression. A random forest model was used to screen for important genes. A novel Riskscore model was established using multivariate regression. The model was evaluated based on the metabolic pathway, immune infiltration, immune checkpoint, and clinical characteristics. Results. According to metabolism-related genes, kidney clear cell carcinoma (KIRC) datasets downloaded from TCGA were clustered into two groups and showed significant differences in prognosis and immune infiltration. There were 667 differentially expressed genes between the two clusters, of which 408 were screened by univariate analysis. Finally, 12 differentially expressed genes (MDK, SLC1A1, SGCB, C4orf3, MALAT1, PILRB, IGHG1, FZD1, IFITM1, MUC20, KRT80, and SALL1) were filtered out using the random forest model. The model of Riskscore was obtained by multiplying the expression levels of these 12 genes with the corresponding coefficients of the multivariate regression. We found that the Riskscore correlated with the expression of these 12 genes; the high Riskscore matched the low survival rate verified in the verification set. The analysis found that the Riskscore model was associated with most of the metabolic processes, immune infiltration of cells such as plasma cells, immune checkpoints such as PD-1, and clinical characteristics such as M stage. Conclusion. We established a new Riskscore model for the prognosis of ccRCC based on metabolism. The genes in the model provided several novel targets for the study of ccRCC.

<b><i>Introduction:</i></b> Transforming growth factor-β (TGF-β), a common outcome of various progressive chronic kidney diseases, can regulate and induce fibrosis. <b><i>Objective:</i></b> The study aimed to identify downstream targets of lncRNA ENST00000453774.1 (lnc453774.1) and outline their functions on the development of renal fibrosis. <b><i>Methods:</i></b> HK-2 cells were induced with 5 ng/mL TGF-β1 for 24 h to construct a renal fibrosis cell model. Differentially expressed genes (DEGs) targeted by lnc453774.1 in TGF-β1-induced renal fibrosis were identified using RNA sequencing. The dataset GSE23338 was employed to identify DEGs in 48-h TGF-β1-stimulated human kidney epithelial cells, and these DEGs were intersected with genes in the key module using weighted gene co-expression network analysis to generate key genes associated with renal fibrosis. MicroRNAs (miRs) that had targeting relationship with keys genes and lnc453774.1 were predicted by using Miranda software, and important genes were intersected with key genes that had targeting relationship with these miRs. Key target genes by lnc453774.1 were identified in a protein-protein interaction network among lnc453774.1, important genes, and reported genes related to autophagy, oxidative stress, and cell adhesion. <b><i>Results:</i></b> Key genes in the key module (turquoise) were intersected with DEGs in the dataset GSE23338 and yielded 20 key genes regulated by lnc453774.1 involved in renal fibrosis. Fourteen miRs had targeting relationship with lnc453774.1 and key genes, and 8 important genes targeted by these 14 miRs were identified. Fibrillin-1 (FBN1), insulin-like growth factor 1 receptor (IGF1R), and Kruppel-like factor 7 (KLF7) were identified to be involved in autophagy, oxidative stress, and cell adhesion and were elevated in the lnc453774.1-overexpressing TGF-β1-induced cells. <b><i>Conclusion:</i></b> These results show FBN1, IGF1R, and KLF7 serve as downstream targets of lnc453774.1, and that lnc453774.1 may protect against renal fibrosis through competing endogenous miRs which target FBN1, IGF1R, and KLF7 mRNAs.