Increased arterial stiffness is strongly associated with cardiovascular morbidity and mortality in dialysis patients. Ischemia-modified albumin (IMA) is a useful biomarker of cardiac ischemia. This study was aimed to explore the association between IMA and arterial stiffness in hemodialysis patients. An observational study was conducted with 120 hemodialysis patients. Clinical data and laboratory characteristics were collected. Arterial stiffness was evaluated by brachial-ankle pulse wave velocity (baPWV). Hemodialysis patients had extensive arterial stiffness and high levels of IMA. Comparing to hemodialysis patients with normal baPWV, those with high baPWV had significantly higher levels of IMA (93.7 ± 8.6 versus 73.1 ± 10.7 Ku/L, P = 0.027). The multiple linear regression analysis showed that IMA was significantly associated with arterial stiffness in hemodialysis patients (β = 0.43, P < 0.001). Moreover, IMA, with a threshold value of 90.4 Ku/L, provided 77.4% sensitivity and 86.6% specificity for predicting arterial stiffness. Hemodialysis patients with arterial stiffness had high levels of IMA. IMA was a good predictive marker of arterial stiffness for hemodialysis patients.

Background Metabolic genes have played a significant role in tumor development and prognosis. In this study, we constructed a metabolic risk model to predict the prognosis of colon cancer based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Methods We downloaded gene expression profile from TCGA database and retrieved differentially expressed metabolic genes. Then we conducted univariate cox regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis to identify prognosis-related genes and construct the metabolic risk model. Then we validated the risk model in TCGA and GEO datasets by Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC), risk score, univariate and multivariate cox regression analysis. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and GO (Gene Ontology) enrichment analyses were conducted to reveal the biological processes and pathways of genes by Gene Set Enrichment Analysis (GSEA). Results We extracted 753 metabolic genes and identified 139 differentially expressed metabolic genes from TCGA database. Then 15 prognostic genes were dug out and 8 genes were filtered into LASSO cox regression analysis. An eight-gene prognostic model was constructed after 1000 resamples. The gene signature has been proved to have an excellent ability to predict prognosis by validation based on TCGA and GEO database. Finally, GSEA showed that multiplex metabolism pathways correlated with colon cancer. Conclusion We identified eight metabolic prognostic genes and developed a metabolic risk model based on TCGA and GEO database to predict overall survival rate of colon cancer.