Early diagnosis of inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), remains a clinical challenge with current tests being invasive and costly. The analysis of volatile organic compounds (VOCs) in exhaled breath and biomarkers in stool (faecal calprotectin (FCP)) show increasing potential as non-invasive diagnostic tools. The aim of this pilot study is to evaluate the efficacy of breath analysis and determine if FCP can be used as an additional non-invasive parameter to supplement breath results, for the diagnosis of IBD. Thirty-nine subjects were recruited (14 CD, 16 UC, 9 controls). Breath samples were analysed using an in-house built electronic nose (Wolf eNose) and commercial gas chromatograph–ion mobility spectrometer (G.A.S. BreathSpec GC-IMS). Both technologies could consistently separate IBD and controls [AUC ± 95%, sensitivity, specificity], eNose: [0.81, 0.67, 0.89]; GC-IMS: [0.93, 0.87, 0.89]. Furthermore, we could separate CD from UC, eNose: [0.88, 0.71, 0.88]; GC-IMS: [0.71, 0.86, 0.62]. Including FCP did not improve distinction between CD vs. UC; eNose: [0.74, 1.00, 0.56], but rather, improved separation of CD vs. controls and UC vs. controls; eNose: [0.77, 0.55, 1.00] and [0.72, 0.89, 0.67] without FCP, [0.81, 0.73, 0.78] and [0.90, 1.00, 0.78] with FCP, respectively. These results confirm the utility of breath analysis to distinguish between IBD-related diagnostic groups. FCP does not add significant diagnostic value to breath analysis within this study.

Urinary VOC was analyzed using an Owlstone (Lonestar, Cambridge, UK) instrument based on ion mobility spectrometry (IMS). Patient information is summarized in Table 1. Midstream urine samples were collected and frozen at –80°C within 4 hours of collection for long-term storage. Before analysis, each sample was defrosted and heated to 40°C for 10 minutes. The air above the sample (known as the headspace) was then passed into the Lonestar unit and the ion mobility measured. Each sample typically takes 5 minutes to analyze (60 seconds per sample to scan, with 5 repeats). Once all the samples were analyzed, an existing data processing pipeline with 4 different classifiers was applied (sparse logistic regression, random forest, gaussian process classifier, and support vector machine). Age was not deemed to be a confounder based on our previous published work (see the Supplementary Material). PDAC urine samples were detected with a sensitivity of 0.91 (95% confidence interval [CI], 0.83-0.96) and specificity of 0.83 (95% CI, 0.73-0.90), with an area under the curve (AUC) of 0.92 (95% CI, 0.88-0.96), using a support vector machine algorithm (Figure 1C). The results were validated by random data splitting into the training and test set achieving similar results with AUC of 0.92 (95% CI, 0.85-0.98), and sensitivity and specificity of 0.90 (95% CI, 0.74-0.98) and 0.81 (95% CI, 0.63-0.93), respectively. We also compared early stage disease (I/II) with healthy individuals as well as early stage I/II with late stage disease (III/IV) using the same analysis pipeline. These analyses revealed a sensitivity of 0.91 (95% CI, 0.78-0.97) and specificity of 0.78 (95% CI, 0.69-0.86), with an AUC of 0.89 (95% CI, 0.83-0.94), a sensitivity of 0.82 (95% CI, 0.67-0.92), and a specificity of 0.89 (95% CI, 0.75-0.97) with an AUC of 0.92 (95% CI, 0.86-0.97), respectively, again using a support vector machine algorithm (Figure 1D, E). Table 1 Demographic Information Healthy Controls (n = 81) Pancreatic Cancer (n = 81) Age (mean ± SD) 51.4 ± 10.6 64.3 ± 23.7 Male sex (%) 30.9 53.1 Stage (n) ∗ One case could not be assessed. I N/A 4 IIA N/A 5 IIB N/A 35 III N/A 24 IV N/A 12 N/A, not applicable; SD, standard deviation. ∗ One case could not be assessed. Open table in a new tab Figure 1 A, Representative image of field asymmetric ion mobility spectroscopy output showing the plume of ion dispersion from a single urine sample of a healthy control. A.U., arbitrary units. ⁵ Covington J.A. van der Schee M.P. Edge A.S. et al. The application of FAIMS gas analysis in medical diagnostics. Analyst. 2015; 140 : 6775-6781 Crossref PubMed Google Scholar B, A similar plume of ion dispersion from a single urine sample of a patient with PDAC. C, Receiver operating characteristic curve showing performance of volatile organic compounds in differentiating healthy individuals from PDAC with an AUC of 0.92 (95% CI, 0.88-0.96), sensitivity of 0.91 (95% CI, 0.83-0.96), and specificity of 0.83 (95% CI, 0.73-0.90). D, Healthy individuals from early stage I/II with an AUC of 0.89 (95% CI, 0.83-0.94), a sensitivity of 0.91 (95% CI, 0.78-0.97), and a specificity of 0.78 (95% CI, 0.69-0.86). E, Early stage PDAC (I/II) could also be successfully separated from advanced stage PDAC (III/IV) with an AUC of 0.92 (95% CI, 0.86-0.97), sensitivity of 0.82 (95% CI, 0.67-0.92), and specificity of 0.89 (95% CI, 0.75-0.97). View Large Image Figure Viewer Download Hi-res image Download (PPT)

In this paper, we report on an in-house developed electronic nose (E-nose) for use with breath analysis. The unit consists of an array of 10 micro-electro-mechanical systems (MEMS) metal oxide (MOX) gas sensors produced by seven manufacturers. Breath sampling of end-tidal breath is achieved using a heated sample tube, capable of monitoring sampling-related parameters, such as carbon dioxide (CO₂), humidity, and temperature. A simple mobile app was developed to receive real-time data from the device, using Wi-Fi communication. The system has been tested using chemical standards and exhaled breath samples from healthy volunteers, before and after taking a peppermint capsule. Results from chemical testing indicate that we can separate chemical standards (acetone, isopropanol and 1-propanol) and different concentrations of isobutylene. The analysis of exhaled breath samples demonstrate that we can distinguish between pre- and post-consumption of peppermint capsules; area under the curve (AUC): 0.81, sensitivity: 0.83 (0.59–0.96), specificity: 0.72 (0.47–0.90), p-value: <0.001. The functionality of the developed device has been demonstrated with the testing of chemical standards and a simplified breath study using peppermint capsules. It is our intention to deploy this system in a UK hospital in an upcoming breath research study.

The electronic nose (eNose) is an instrument designed to mimic the human olfactory system. Usage of eNose in medical applications is more popular than ever, due to its low costs and non-invasive nature. The eNose sniffs the gases and vapours that emanate from human waste (urine, breath, and stool) for the diagnosis of variety of diseases. Diabetes mellitus type 2 (DM2) affects 8.3% of adults in the world, with 43% being underdiagnosed, resulting in 4.9 million deaths per year. In this study, we investigated the potential of urinary volatile organic compounds (VOCs) as novel non-invasive diagnostic biomarker for diabetes. In addition, we investigated the influence of sample age on the diagnostic accuracy of urinary VOCs. We analysed 140 urine samples (73 DM2, 67 healthy) with Field-Asymmetric Ion Mobility Spectrometry (FAIMS); a type of eNose; and FOX 4000 (AlphaM.O.S, Toulouse, France). Urine samples were collected at UHCW NHS Trust clinics over 4 years and stored at −80 °C within two hours of collection. Four different classifiers were used for classification, specifically Sparse Logistic Regression, Random Forest, Gaussian Process, and Support Vector on both FAIMS and FOX4000. Both eNoses showed their capability of diagnosing DM2 from controls and the effect of sample age on the discrimination. FAIMS samples were analysed for all samples aged 0–4 years (AUC: 88%, sensitivity: 87%, specificity: 82%) and then sub group samples aged less than a year (AUC (Area Under the Curve): 94%, Sensitivity: 92%, specificity: 100%). FOX4000 samples were analysed for all samples aged 0–4 years (AUC: 85%, sensitivity: 77%, specificity: 85%) and a sub group samples aged less than 18 months: (AUC: 94%, sensitivity: 90%, specificity: 89%). We demonstrated that FAIMS and FOX 4000 eNoses can discriminate DM2 from controls using urinary VOCs. In addition, we showed that urine sample age affects discriminative accuracy.

Surgical site infection represents a large burden of care in the National Health Service. Current methods for diagnosis include a subjective clinical assessment and wound swab culture that may take several days to return a result. Both techniques are potentially unreliable and result in delays in using targeted antibiotics. Volatile organic compounds (VOCs) are produced by micro-organisms such as those present in an infected wound. This study describes the use of a device to differentiate VOCs produced by an infected wound vs. colonised wound. Malodourous wound dressings were collected from patients, these were a mix of post-operative wounds and vascular leg ulcers. Wound microbiology swabs were taken and antibiotics commenced as clinically appropriate. A control group of soiled, but not malodorous wound dressings were collected from patients who had a split skin graft (SSG) donor site. The analyser used was a G.A.S. GC-IMS. The results from the samples had a sensitivity of 100% and a specificity of 88%, with a positive predictive value of 90%. An area under the curve (AUC) of 91% demonstrates an excellent ability to discriminate those with an infected wound from those without. VOC detection using GC-IMS has the potential to serve as a diagnostic tool for the differentiation of infected and non-infected wounds and facilitate the treatment of wound infections that is cost effective, non-invasive, acceptable to patients, portable, and reliable.

Colorectal symptoms are common but only infrequently represent serious pathology, including colorectal cancer (CRC). A large number of invasive tests are presently performed for reassurance. We investigated the feasibility of urinary volatile organic compound (VOC) testing as a potential triage tool in patients fast-tracked for assessment for possible CRC. A prospective, multi-center, observational feasibility study was performed across three sites. Patients referred to NHS fast-track pathways for potential CRC provided a urine sample that underwent Gas Chromatography-Mass Spectrometry (GC-MS), Field Asymmetric Ion Mobility Spectrometry (FAIMS), and Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) analysis. Patients underwent colonoscopy and/or CT colonography and were grouped as either CRC, adenomatous polyp(s), or controls to explore the diagnostic accuracy of VOC output data supported by an artificial neural network (ANN) model. 558 patients participated with 23 (4%) CRC diagnosed. 59% of colonoscopies and 86% of CT colonographies showed no abnormalities. Urinary VOC testing was feasible, acceptable to patients, and applicable within the clinical fast track pathway. GC-MS showed the highest clinical utility for CRC and polyp detection vs. controls (sensitivity = 0.878, specificity = 0.882, AUROC = 0.896) but it is labour intensive. Urinary VOC testing and analysis are feasible within NHS fast-track CRC pathways. Clinically meaningful differences between patients with cancer, polyps, or no pathology were identified suggesting VOC analysis may have future utility as a triage tool.