Literature DB >> 28487129

Identification of potential diagnostic biomarkers of acute pancreatitis by serum metabolomic profiles.

Hong Xiao1, Jian-Hua Huang2, Xing-Wen Zhang3, Rida Ahmed4, Qing-Ling Xie1, Bin Li1, Yi-Ming Zhu5, Xiong Cai6, Qing-Hua Peng6, Yu-Hui Qin1, Hui-Yong Huang6, Wei Wang7.   

Abstract

Acute pancreatitis (AP) is defined as an acute inflammation of pancreas that may cause damage to other tissues and organs depending upon the severity of symptoms. The diagnosis of AP is usually made by detection of raised circulating pancreatic enzyme levels, but there are occasional false positive and false negative diagnoses and such tests are often normal in delayed presentations. More accurate biomarkers would help in such situations. In this study, the global metabolites' changes of AP patients (APP) were profiled by using gas chromatography-mass spectrometry (GC-MS). Multivariate pattern recognition techniques were used to establish the classification models to distinguish APP from healthy participants (HP). Some significant metabolites including 3-hydroxybutyric acid, phosphoric acid, glycerol, citric acid, d-galactose, d-mannose, d-glucose, hexadecanoic acid and serotonin were selected as potential biomarkers for helping clinical diagnosis of AP. Furthermore, the metabolite changes in APP with severe and mild symptoms were also analyzed. Based on the selected biomarkers, some relevant pathways were also identified. Our results suggested that GC-MS based serum metabolomics method can be used in the clinical diagnosis of AP by profiling potential biomarkers.
Copyright © 2017 IAP and EPC. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Acute pancreatitis; Biomarker discovery; Metabolomics; Pattern recognition

Year:  2017        PMID: 28487129     DOI: 10.1016/j.pan.2017.04.015

Source DB:  PubMed          Journal:  Pancreatology        ISSN: 1424-3903            Impact factor:   3.996


  7 in total

Review 1.  Multifactorial Scores and Biomarkers of Prognosis of Acute Pancreatitis: Applications to Research and Practice.

Authors:  Pedro Silva-Vaz; Ana Margarida Abrantes; Miguel Castelo-Branco; António Gouveia; Maria Filomena Botelho; José Guilherme Tralhão
Journal:  Int J Mol Sci       Date:  2020-01-04       Impact factor: 5.923

2.  Metabolite Markers for Characterizing Sasang Constitution Type through GC-MS and 1H NMR-Based Metabolomics Study.

Authors:  Eun-Ju Kim; Young-Shick Hong; Seung-Ho Seo; Seong-Eun Park; Chang-Su Na; Hong-Seok Son
Journal:  Evid Based Complement Alternat Med       Date:  2019-02-03       Impact factor: 2.629

3.  Quantitative metabolic analysis of plasma extracellular vesicles for the diagnosis of severe acute pancreatitis.

Authors:  Doudou Lou; Keqing Shi; Hui-Ping Li; Qingfu Zhu; Liang Hu; Jiaxin Luo; Rui Yang; Fei Liu
Journal:  J Nanobiotechnology       Date:  2022-01-28       Impact factor: 10.435

4.  Correlation between metabolomic profile constituents and feline pancreatic lipase immunoreactivity.

Authors:  Magdalena Maria Krasztel; Michał Czopowicz; Olga Szaluś-Jordanow; Agata Moroz; Marcin Mickiewicz; Jarosław Kaba
Journal:  J Vet Intern Med       Date:  2022-01-13       Impact factor: 3.333

5.  Identification of potential diagnostic biomarkers of cerebral infarction using gas chromatography-mass spectrometry and chemometrics.

Authors:  Ming-Jiao Li; Hong Xiao; Yi-Xing Qiu; Jian-Hua Huang; Rong-Yong Man; Yan Qin; Guang-Hua Xiong; Qing-Hua Peng; Yu-Qing Jian; Cai-Yun Peng; Wei-Ning Zhang; Wei Wang
Journal:  RSC Adv       Date:  2018-06-21       Impact factor: 4.036

Review 6.  Pancreatic Disorders in Children with Inflammatory Bowel Disease.

Authors:  Piotr Jakimiec; Katarzyna Zdanowicz; Kamila Kwiatek-Sredzinska; Aleksandra Filimoniuk; Dariusz Lebensztejn; Urszula Daniluk
Journal:  Medicina (Kaunas)       Date:  2021-05-11       Impact factor: 2.430

7.  Identification and validation of a multivariable prediction model based on blood plasma and serum metabolomics for the distinction of chronic pancreatitis subjects from non-pancreas disease control subjects.

Authors:  M Gordian Adam; Georg Beyer; Julia Mayerle; Markus M Lerch; Nicole Christiansen; Beate Kamlage; Christian Pilarsky; Marius Distler; Tim Fahlbusch; Ansgar Chromik; Fritz Klein; Marcus Bahra; Waldemar Uhl; Robert Grützmann; Ujjwal M Mahajan; Frank U Weiss
Journal:  Gut       Date:  2021-02-04       Impact factor: 23.059

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.