A urine and serum metabolomics study of gastroesophageal reflux disease in TCM syndrome differentiation using UPLC-Q-TOF/MS

格尔德 代谢组学 尿 化学 胃肠病学 内科学 回流 代谢组 内分泌学 疾病 医学 色谱法
作者
Xinxin Ye,Xiaoqun Wang,Yingfeng Wang,Wenting Sun,Yang Chen,Dan Wang,Zhihong Li,Zhong‐Feng Li
出处
期刊:Journal of Pharmaceutical and Biomedical Analysis [Elsevier BV]
卷期号:206: 114369-114369 被引量:7
标识
DOI:10.1016/j.jpba.2021.114369
摘要

Gastroesophageal reflux disease (GERD) is a common, chronic and complex upper gastrointestinal disease. In Traditional Chinese medicine (TCM) theory, GERD is classified into two main types: stagnant heat of liver and stomach (SHLS) and deficient cold of spleen and stomach (DCSS). The discovery and evaluation of potential biomarkers for different syndrome types of GERD may contribute to comprehend specific molecular mechanism and identify new targets for diagnosis and appropriate management. In our study, 60 subjects including 40 GERD patients (20 SHLS and 20 DCSS) and 20 healthy controls were recruited, and the serum and urine metabolic profiles from untargeted liquid chromatography coupled to mass spectrometry (LC-MS) metabolomics approach were obtained. Finally 38 biomarkers associated with disease were identified and 9 metabolic pathways were enriched. The most enriched pathways were amino acid metabolism, steroid hormone biosynthesis, glycerophospholipid metabolism, sphingolipid metabolism and TCA cycle. According to the area under curve (AUC) value, we propose a cohort of three metabolites from urine and serum samples as promising biomarkers for TCM syndrome differentiation of GERD, which are prolylhydroxyproline, glycitein-4'-O-glucuronide, capsianoside I in urine and neuAcalpha2-3Galbeta-Cer (d18:1/16:0), sphinganine, arachidonic acid in serum. The cumulative AUC value of merged biomarkers in urine and serum was 0.979 (95%CI 0.927-1) and 0.842 (95%CI 0.704-0.980), respectively. The results indicated that LC-MS based metabolomic profiling method might be an effective and promising tool on further pathogenesis discovering of GERD. The findings provided new strategy for the diagnosis of GERD TCM syndrome differentiation in clinic.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
堪尔风完成签到 ,获得积分10
刚刚
1秒前
迅速的仰发布了新的文献求助10
1秒前
小圈圈梦魇完成签到,获得积分10
1秒前
PJT-8450完成签到,获得积分10
2秒前
zho发布了新的文献求助10
2秒前
郭星星完成签到,获得积分10
3秒前
3秒前
谨慎雪碧完成签到 ,获得积分10
5秒前
静心完成签到,获得积分10
6秒前
风秋杨完成签到 ,获得积分0
7秒前
追光少年完成签到,获得积分10
8秒前
鲁滨逊发布了新的文献求助10
9秒前
10秒前
迅速的仰完成签到,获得积分10
11秒前
Jasper应助shouyu29采纳,获得10
13秒前
不会学习的小郭完成签到 ,获得积分10
14秒前
14秒前
pp发布了新的文献求助10
15秒前
Neltharion完成签到,获得积分10
15秒前
16秒前
Leif举报小郭求助涉嫌违规
16秒前
薄荷味的soda完成签到,获得积分10
18秒前
正直的广缘完成签到 ,获得积分10
20秒前
TJJJJJ发布了新的文献求助10
20秒前
瀛瀛完成签到 ,获得积分10
20秒前
Huimin完成签到,获得积分10
20秒前
lxlcx应助Migrol采纳,获得20
21秒前
健壮的思枫完成签到,获得积分10
22秒前
22秒前
英俊的铭应助化工小蠕虫采纳,获得10
23秒前
24秒前
tuzi完成签到,获得积分10
25秒前
25秒前
hbkj完成签到,获得积分10
25秒前
25秒前
D-L@rabbit完成签到 ,获得积分10
27秒前
njzhangyanyang完成签到,获得积分10
27秒前
meng完成签到 ,获得积分10
28秒前
英姑应助pp采纳,获得10
29秒前
高分求助中
Thinking Small and Large 500
Algorithmic Mathematics in Machine Learning 500
Mapping the Stars: Celebrity, Metonymy, and the Networked Politics of Identity 400
Getting Published in SSCI Journals: 200+ Questions and Answers for Absolute Beginners 300
Engineering the boosting of the magnetic Purcell factor with a composite structure based on nanodisk and ring resonators 240
Study of enhancing employee engagement at workplace by adopting internet of things 200
Minimum Bar Spacing as a Function of Bond and Shear Strength 200
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3837587
求助须知:如何正确求助?哪些是违规求助? 3379741
关于积分的说明 10510291
捐赠科研通 3099357
什么是DOI,文献DOI怎么找? 1707079
邀请新用户注册赠送积分活动 821427
科研通“疑难数据库(出版商)”最低求助积分说明 772615