Potential metabolomic biomarkers for reliable diagnosis of Behcet's disease using gas chromatography/ time-of-flight-mass spectrometry

Joong Kyong Ahn, Jungyeon Kim, Jiwon Hwang, Juhwan Song, Kyoung Heon Kim, Hoon Suk Cha

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Objectives: Although many diagnostic criteria of Behcet's disease (BD) have been developed and revised by experts, diagnosing BD is still complicated and challenging. No metabolomic studies on serum have been attempted to improve the diagnosis and to identify potential biomarkers of BD. The purposes of this study were to investigate distinctive metabolic changes in serum samples of BD patients and to identify metabolic candidate biomarkers for reliable diagnosis of BD using the metabolomics platform. Methods: Metabolomic profiling of 90 serum samples from 45 BD patients and 45 healthy controls (HCs) were performed via gas chromatography with time-of-flight mass spectrometry (GC/TOF-MS) with multivariate statistical analyses. Results: A total of 104 metabolites were identified from samples. The serum metabolite profiles obtained from GC/TOF-MS analysis can distinguish BD patients from HC group in discovery set. The variation values of the partial least squared-discrimination analysis (PLS-DA) model are R2X of 0.246, R2Y of 0.913 and Q2 of 0.852, respectively, indicating strong explanation and prediction capabilities of the model. A panel of five metabolic biomarkers, namely, decanoic acid, fructose, tagatose, linoleic acid and oleic acid were selected and adequately validated as putative biomarkers of BD (sensitivity 100%, specificity 97.1%, area under the curve 0.998) in the discovery set and independent set. The PLS_DA model showed clear discrimination of BD and HC groups by the five metabolic biomarkers in independent set. Conclusions: This is the first report on characteristic metabolic profiles and potential metabolite biomarkers in serum for reliable diagnosis of BD using GC/TOF-MS.

Original languageEnglish
JournalJoint Bone Spine
DOIs
Publication statusAccepted/In press - 2017

Fingerprint

Metabolomics
Behcet Syndrome
Gas Chromatography
Mass Spectrometry
Biomarkers
Serum
Control Groups
Metabolome
Linoleic Acid
Oleic Acid
Fructose
Area Under Curve
Multivariate Analysis
Sensitivity and Specificity

Keywords

  • Behcet's disease
  • Biomarker
  • Gas chromatography-mass spectrometry
  • Metabolomics
  • Serum

ASJC Scopus subject areas

  • Rheumatology

Cite this

Potential metabolomic biomarkers for reliable diagnosis of Behcet's disease using gas chromatography/ time-of-flight-mass spectrometry. / Ahn, Joong Kyong; Kim, Jungyeon; Hwang, Jiwon; Song, Juhwan; Kim, Kyoung Heon; Cha, Hoon Suk.

In: Joint Bone Spine, 2017.

Research output: Contribution to journalArticle

@article{9d54a0eb71a446c2829d74569d93f6d5,
title = "Potential metabolomic biomarkers for reliable diagnosis of Behcet's disease using gas chromatography/ time-of-flight-mass spectrometry",
abstract = "Objectives: Although many diagnostic criteria of Behcet's disease (BD) have been developed and revised by experts, diagnosing BD is still complicated and challenging. No metabolomic studies on serum have been attempted to improve the diagnosis and to identify potential biomarkers of BD. The purposes of this study were to investigate distinctive metabolic changes in serum samples of BD patients and to identify metabolic candidate biomarkers for reliable diagnosis of BD using the metabolomics platform. Methods: Metabolomic profiling of 90 serum samples from 45 BD patients and 45 healthy controls (HCs) were performed via gas chromatography with time-of-flight mass spectrometry (GC/TOF-MS) with multivariate statistical analyses. Results: A total of 104 metabolites were identified from samples. The serum metabolite profiles obtained from GC/TOF-MS analysis can distinguish BD patients from HC group in discovery set. The variation values of the partial least squared-discrimination analysis (PLS-DA) model are R2X of 0.246, R2Y of 0.913 and Q2 of 0.852, respectively, indicating strong explanation and prediction capabilities of the model. A panel of five metabolic biomarkers, namely, decanoic acid, fructose, tagatose, linoleic acid and oleic acid were selected and adequately validated as putative biomarkers of BD (sensitivity 100{\%}, specificity 97.1{\%}, area under the curve 0.998) in the discovery set and independent set. The PLS_DA model showed clear discrimination of BD and HC groups by the five metabolic biomarkers in independent set. Conclusions: This is the first report on characteristic metabolic profiles and potential metabolite biomarkers in serum for reliable diagnosis of BD using GC/TOF-MS.",
keywords = "Behcet's disease, Biomarker, Gas chromatography-mass spectrometry, Metabolomics, Serum",
author = "Ahn, {Joong Kyong} and Jungyeon Kim and Jiwon Hwang and Juhwan Song and Kim, {Kyoung Heon} and Cha, {Hoon Suk}",
year = "2017",
doi = "10.1016/j.jbspin.2017.05.019",
language = "English",
journal = "Revue du Rhumatisme (English Edition)",
issn = "1169-8446",
publisher = "Elsevier Masson",

}

TY - JOUR

T1 - Potential metabolomic biomarkers for reliable diagnosis of Behcet's disease using gas chromatography/ time-of-flight-mass spectrometry

AU - Ahn, Joong Kyong

AU - Kim, Jungyeon

AU - Hwang, Jiwon

AU - Song, Juhwan

AU - Kim, Kyoung Heon

AU - Cha, Hoon Suk

PY - 2017

Y1 - 2017

N2 - Objectives: Although many diagnostic criteria of Behcet's disease (BD) have been developed and revised by experts, diagnosing BD is still complicated and challenging. No metabolomic studies on serum have been attempted to improve the diagnosis and to identify potential biomarkers of BD. The purposes of this study were to investigate distinctive metabolic changes in serum samples of BD patients and to identify metabolic candidate biomarkers for reliable diagnosis of BD using the metabolomics platform. Methods: Metabolomic profiling of 90 serum samples from 45 BD patients and 45 healthy controls (HCs) were performed via gas chromatography with time-of-flight mass spectrometry (GC/TOF-MS) with multivariate statistical analyses. Results: A total of 104 metabolites were identified from samples. The serum metabolite profiles obtained from GC/TOF-MS analysis can distinguish BD patients from HC group in discovery set. The variation values of the partial least squared-discrimination analysis (PLS-DA) model are R2X of 0.246, R2Y of 0.913 and Q2 of 0.852, respectively, indicating strong explanation and prediction capabilities of the model. A panel of five metabolic biomarkers, namely, decanoic acid, fructose, tagatose, linoleic acid and oleic acid were selected and adequately validated as putative biomarkers of BD (sensitivity 100%, specificity 97.1%, area under the curve 0.998) in the discovery set and independent set. The PLS_DA model showed clear discrimination of BD and HC groups by the five metabolic biomarkers in independent set. Conclusions: This is the first report on characteristic metabolic profiles and potential metabolite biomarkers in serum for reliable diagnosis of BD using GC/TOF-MS.

AB - Objectives: Although many diagnostic criteria of Behcet's disease (BD) have been developed and revised by experts, diagnosing BD is still complicated and challenging. No metabolomic studies on serum have been attempted to improve the diagnosis and to identify potential biomarkers of BD. The purposes of this study were to investigate distinctive metabolic changes in serum samples of BD patients and to identify metabolic candidate biomarkers for reliable diagnosis of BD using the metabolomics platform. Methods: Metabolomic profiling of 90 serum samples from 45 BD patients and 45 healthy controls (HCs) were performed via gas chromatography with time-of-flight mass spectrometry (GC/TOF-MS) with multivariate statistical analyses. Results: A total of 104 metabolites were identified from samples. The serum metabolite profiles obtained from GC/TOF-MS analysis can distinguish BD patients from HC group in discovery set. The variation values of the partial least squared-discrimination analysis (PLS-DA) model are R2X of 0.246, R2Y of 0.913 and Q2 of 0.852, respectively, indicating strong explanation and prediction capabilities of the model. A panel of five metabolic biomarkers, namely, decanoic acid, fructose, tagatose, linoleic acid and oleic acid were selected and adequately validated as putative biomarkers of BD (sensitivity 100%, specificity 97.1%, area under the curve 0.998) in the discovery set and independent set. The PLS_DA model showed clear discrimination of BD and HC groups by the five metabolic biomarkers in independent set. Conclusions: This is the first report on characteristic metabolic profiles and potential metabolite biomarkers in serum for reliable diagnosis of BD using GC/TOF-MS.

KW - Behcet's disease

KW - Biomarker

KW - Gas chromatography-mass spectrometry

KW - Metabolomics

KW - Serum

UR - http://www.scopus.com/inward/record.url?scp=85021159686&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85021159686&partnerID=8YFLogxK

U2 - 10.1016/j.jbspin.2017.05.019

DO - 10.1016/j.jbspin.2017.05.019

M3 - Article

JO - Revue du Rhumatisme (English Edition)

JF - Revue du Rhumatisme (English Edition)

SN - 1169-8446

ER -