关键词: 腹膜透析, 腹膜纤维化, 非靶向代谢组学, 代谢产物, 代谢途径

Abstract: Objective Using untargeted metabolomics technology, this study analyzed the differential metabolites and metabolic pathways in the dialysate of long-term peritoneal dialysis (PD) patients with peritoneal dysfunction versus short-term PD patients with normal peritoneal function, and aimed to identify potential metabolic biomarkers and intervention targets for peritoneal fibrosis (PF).  Methods  A total of 26 PD patients were included and divided into two groups: the long-term group with peritoneal dysfunction (net ultrafiltration volume of 2.5% peritoneal dialysate at 4 hours＜100 ml, dialysis vintage >6 years, n=13) and the short-term group with normal peritoneal function (dialysis vintage ＜12 months, n=13). Demographic characteristics and clinical data of the patients were collected. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to detect and analyze metabolites in the dialysate. The differential metabolites were screened. Metabolic pathway annotation and enrichment analysis were performed for the differential metabolites.  Results  Compared to the short-term group, the long-term group with peritoneal dysfunction had lower serum albumin (t=-2.320，P=0.029), and higher levels of serum creatinine (t=2.723，P=0.012) and blood urea nitrogen (t=2.231，P=0.026). The long-term group also showed lower 4-hour ultrafiltration volume (t=-8.308, P＜0.001) and higher 4-hour dialysate-to-plasma creatinine ratio (D/Pcr) (t=3.037, P=0.006). Compared to the short-term group, the long-term group had 2040 up-regulated metabolites and 992 down-regulated metabolites. Furthermore, 2-ketobutyric acid, indoxyl sulfate and other metabolites increased in the long-term group. Metabolites such as serine, L-carnitine, and butyrolactone II were more abundant in the short-term group. Pathway annotation of differential metabolites revealed enrichment in amino acid metabolism, mainly phenylalanine metabolism, and carbohydrate metabolism, including propionate metabolism, citric acid cycle, etc. Further analysis of the metabolic pathways identified phenylalanine metabolism, glycerate and dicarboxylic acid metabolism, and the citric acid cycle as key pathways for subsequent research.  Conclusion The analysis of differential metabolites and metabolic pathways in the effluent of PD patients with different peritoneal functions based on non-targeted metabolomics provides key information and a new perspective for identifying biomarkers and intervention targets for PF.

Key words: Peritoneal dialysis, Peritoneal fibrosis, Non-targeted metabolomics, Metabolic products, Metabolic pathways