Inflammatory Bowel Disease

The pathogenesis of IBD involves bacteria, which ferment non-starch polysaccharides in the colon producing a fermentation profile that through altered gut permeability can be traced from VOCs

Non-invasive exhaled volatile organic biomarker analysis to detect inflammatory bowel disease (IBD)

Ramesh P. Arasaradnam, Michael McFarlane, Emma Daulton, Jim Skinner, Nicola O’Connell, Subiatu Wurie, Samantha Chambers, Chuka Nwokolo, Karna Bardhan, Richard Savage, James Covington

Inflammatory bowel disease (IBD) is a common condition affecting the Western world with approximately 115,000 people in the UK suffering from Crohn’s disease (CD) and 146,000 with ulcerative colitis (UC). The cause of IBD is not entirely understood, but it is widely believed to be the result of complex interactions between an individual’s genetic susceptibility, environmental triggers (e.g. diet, life style, etc.) and the influence of an individual’s gastrointestinal bacterial colonies.

Current diagnostic tools for IBD include fecal inflammatory markers, endoscopic investigations with histological examination, capsule endoscopy and imaging. Despite the diverse array of investigations available, the diagnosis of CD or UC can often be difficult when the disease is limited to the colon, and may rely on histology. Even so, the accepted standard of histology can often fail to distinguish between CD and UC.

The aim of this study was to determine whether volatile organic compounds (VOCs) in exhaled breath could be used to distinguish IBD from healthy controls, and also UC from CD, using a Lonestar gas analyzer.

A total of 76 subjects were recruited for the study, 54 of which had histologically confirmed IBD (29 UC and 25 CD) as well as 22 healthy controls. The predictive performance of the data from FAIMS analysis of exhaled breath samples was studied using a pipeline consisting of wavelet transformation, feature selection and a sparse logistic regression classifier. This was used to classify samples and calculate sensitivities and specificities as part of a 10-fold cross-validation.

This box plot shows the predictive power of IBD (UC & CD) vs controls, UCvs controls, CD vs controls and finally UC vs CD.

inflammatory bowel disease box plot
Predictions of classifier to different combinations of diseases and controls (UC – ulcerative colitis, CD – Crohn’s disease, V – volunteer).


The analysis showed that patients with IBD could be distinguished from control patients using FAIMS analysis of VOCs in breath samples with a sensitivityof 0.74 (95% confidence intervals (CI): 0.65–0.82) and specificity of 0.75 (95% CI: 0.53–0.90), p-value 6.2 × 10−7. The AUROC (areas under receiver operator curve) was 0.82 (95% CI: 0.74–0.89). FAIMS could distinguish those with UC from those with CD with a sensitivity of 0.67 (95% CI:0.54–0.79) and specificity of 0.67 (95% CI: 0.54–0.79), p-value 9.23 × 10−4. The AUROC was 0.70 (95% CI: 0.60–0.80)

Inflammatory bowel disease ROC
Receiver operator curve (ROC) plots of IBD (UC and CD) vs healthy controls.

This study confirms the utility of FAIMS exhaled VOC analysis to distinguish IBD from healthy controls, and UC from CD. It conforms to other studies using different technology, whilst affirming exhaled VOCs as biomarkers for diagnosing IBD.


We have been using FAIMS for almost five years and have found it able to non-invasively detect a broad range of diseases, including cancer, with high sensitivity and specificity

Professor James Covington
University of Warwick

Multiple studies have proven the effectiveness of Lonestar's powerful, data-rich output

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