The Library

OC-163 identification of inflammatory bowel disease (IBD) using field asymmetric ion mobility spectrometry (FAIMS)

Tools

Arasaradnam, Ramesh P., Ouaret, N., Thomas, M. G., Hetherington, E., Quraishi, M. N., Nwokolo, Chuka U., Bardhan, Karna Dev and Covington, James A.. (2012) OC-163 identification of inflammatory bowel disease (IBD) using field asymmetric ion mobility spectrometry (FAIMS). Gut, Vol.61 (Suppl.2). A70. ISSN 0017-5749

Preview

Text
WRAP_Arasaradnam_ModifiedIBD_Final 17 05 12.pdf - Accepted Version
Download (976Kb) | Preview

Official URL: http://dx.doi.org/10.1136/gutjnl-2012-302514a.163

Abstract

Introduction Resident colonic bacteria, principally anaerobes and firmicutes, ferment undigested fibre. The resultant volatile organic compounds (VOCs) formed are dissolved in the faeces but also absorbed and excreted in the urine. We have previously shown that electronic nose (E-nose) analysis of urine VOCs distinguishes between Crohn's disease (CD), ulcerative colitis (UC) and healthy volunteers (HV): the underlying principle is pattern recognition of disease-specific “chemical fingerprint”. High-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) offers a possible alternative. The underlying principle is separation of VOC chemical components based on their different ion mobilties in high electric fields. We performed a pilot study in the above groups, the patients in remission (Rem) or with active disease (AD), to assess if this technology could achieve separation between the groups. The results were validated against E-nose analysis.

Methods 59 subjects were studied; HV n=14, UC (Rem) n=18, UC (AD) n=4; CD (Rem) n=19, CD (AD) n=4. Urine samples (7 ml) in universal containers (25 ml) were heated to 40±0.1 C. The headspace (the air above the sample) was then analysed using FAIMS. The data were analysed by Fisher Discriminant Analysis.

Results The technique distinguished between the three groups. Additionally, patients with active disease could be distinguished from those in remission. These results were concordant with E-nose analysis.

Conclusion This pilot shows that urine VOCs, analysed by the different approaches of E-nose and FAIMS, the latter a novel application, can distinguish the healthy from those with UC and CD when disease is active or in remission. The two technologies together offer a non-invasive approach to diagnosis and follow-up in inflammatory bowel disease.

Item Type:

Journal Article

Subjects:

R Medicine > RC Internal medicine
T Technology > TP Chemical technology

Divisions:

Faculty of Science > Engineering
Faculty of Science > Molecular Organisation and Assembly in Cells (MOAC)
Faculty of Medicine > Warwick Medical School

Library of Congress Subject Headings (LCSH):

Inflammatory bowel diseases -- Pathogenesis, Inflammatory bowel diseases -- Diagnosis, Electrochemical sensors -- Diagnostic use

Journal or Publication Title:

Gut

Publisher:

BMJ

ISSN:

0017-5749

Official Date:

2012

Dates:

Date	Event
2012	Published

Volume:

Vol.61

Number:

Suppl.2

Page Range:

A70

Identification Number:

10.1136/gutjnl-2012-302514a.163

Status:

Peer Reviewed

Publication Status:

Published

Access rights to Published version:

Restricted or Subscription Access

Funder:

Broad Foundations

References:

1. Conte, M. P., S. Schippa, I. Zamboni, et al. 2006. Gut-associated bacterial microbiota in paediatric patients with inflammatory bowel disease. Gut 55:1760-1767
2. Arasaradnam RP, Pharaoh MW, Williams GJ et al. Colonic fermentation--more than meets the nose. Med Hypotheses. 2009 Nov;73(5):753-6
3. Arasaradnam R, Quraishi N, Kyrou I, et al. Insights into ‘Fermentonomics’: Evaluation of volatile organic compounds (VOCs) in human disease using an Electronic ‘e’ Nose. J Med Eng Technol. 2011 Feb;35(2):87-91. Epub 2011 Jan 4.
4. Arasaradnam RP, Pharaoh M, Williams G et al. Fermentonomic insights – the gut in health and disease. DDW 2010; Gastroenterology 2010; Abstract #T1267
5. Arasaradnam RP and Bardhan KD. Bioactive foods and Extracts – Cancer treatment and prevention. Taylor Francis, New York 2010
6. Gardner JW, Craven M, Dow C, Hines EL. The prediction of bacteria type and culture growth phase by an electronic nose with a multi-layer perceptron network. Meas Sci Technol 1998;9:120-127
7. Arasaradnam RP, Ouaret N, Joseph M et al. Radiotherapy induced gut damage: identifying ‘at risk’ patients using an electronic ‘e’ nose. Gut 2011;60:A108-A109 doi:10.1136/gut.2011.239301.227
8. Arasaradnam RP, Ouaret N, Thomas MG et al. Evaluation of gut bacterial populations using an Electronic ‘e’ Nose and FAIMS (Field Asymmetric Ion Mobility Spectrometry): Further insights into ‘Fermentonomics’. J Med Eng Technol. 2012 In press
9. Tannock GW. The search for disease associated compositional shifts in bowel bacterial communities of humans. Trends in Microbiology 2008; 16(10):488-495
10. Mareau P. Bacterial flora in Inflammatory bowel disease. Dig Dis 2009;27(1):99-103.
11. Darfeuille-Michaud A, Boudeau J, Bulois P, et al. High prevalence of adherent-invasive Escherichia coli associated with ileal mucosa in Crohn's disease. Gastroenterology 2004 Aug;127(2):412-21.
12. Marchesi JR, Holmes E, Khan F et al. Rapid and non-invasive metabonomic characterisation of inflammatory bowel disease. J Proteome Res 2007; 6:546-551
13. C P Tamboli, C Neut, P Desreumaux, et al. Dysbiosis in inflammatory bowel disease Gut 2004;53:1 1-4 doi:10.1136/gut.53.1.1

URI:

http://wrap.warwick.ac.uk/id/eprint/48535

Data sourced from Thomson Reuters' Web of Knowledge

Request changes or add full text files to a record