局解手术学杂志2025，Vol.34Issue(4)：310-315,6.DOI:10.11659/jjssx.05E024081
代谢组学结合机器学习算法探究心脏瓣膜置换术后早期认知功能障碍生物标志物
Metabolomics combined with machine learning algorithms in exploring biomarkers of early postoperative cognitive dysfunction after heart valve replacement
摘要
Abstract
Objective Metabolomics combined with machine learning algorithms was used to systematically study the preoperative serum metabolites of patients with early postoperative cognitive dysfunction(POCD)after heart valve replacement,so as to screen biomarkers that may predict early POCD after heart valve replacement and explore the corresponding metabolic regulatory mechanisms.Methods A total of 60 patients underwent heart valve replacement under extracorporeal circulation were selected and divided into early-POCD group(group P)and non-POCD group(group N)according to whether POCD occurred or not.Metabolomic analysis was performed on preoperative serum samples of patients in group P and group N to screen the differential metabolites and metabolic pathways.The biomarkers related to early POCD were identified by random forest algorithm.Results A total of 532 differential metabolites were detected by metabonomics analysis,and 5 biomarkers were screened by random forest algorithm,namely quinoline,3'-sialyllactose,sphingomyelin(d18∶1/20∶0),lysophosphatidylcholine[P-18∶1(9Z)]and 25-hydroxycholesterol.Among them,the main metabolic pathways were phenylalanine metabolism,primary bile acid biosynthesis,ascorbic acid and aldonate metabolism,pentose and glucuronate interconversion,tryptophan metabolism,drug metabolism-cytochrome P450,porphyrin and chlorophyll metabolism.Conclusion Many metabolic pathways in patients with early POCD after heart valve replacement under extracorporeal circulation have changed before operation,which may lead to the occurrence of early POCD.Quinoline,3'-sialyllactose,sphingomyelin(d18∶1/20∶0),lysophosphatidylcholine[P-18∶1(9Z)]and 25-hydroxycholesterol may be biomarkers for predicting early POCD.关键词
代谢组学/随机森林算法/心脏手术/早期术后认知功能障碍/生物标志物Key words
metabolomics/random forest algorithm/heart surgery/early postoperative cognitive dysfunction/biomarker分类
医药卫生引用本文复制引用
陈薇,佘汉,唐小唪,陈伟,刘良明,李涛,胡弋..代谢组学结合机器学习算法探究心脏瓣膜置换术后早期认知功能障碍生物标志物[J].局解手术学杂志,2025,34(4):310-315,6.基金项目
国家自然科学基金(82300561) （82300561）
重庆市自然科学基金(CSTB2023NSCQ-MSX0713) （CSTB2023NSCQ-MSX0713）
