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Ferrandino G, De Palo G, Murgia A, Birch O, Tawfike A, Smith R, Debiram-Beecham I, Gandelman O, Kibble G, Lydon AM, Groves A, Smolinska A, Allsworth M, Boyle B, van der Schee MP, Allison M, Fitzgerald RC, Hoare M, Snowdon VK. Breath Biopsy® to Identify Exhaled Volatile Organic Compounds Biomarkers for Liver Cirrhosis Detection. J Clin Transl Hepatol 2023; 11:638-648. [PMID: 36969895 PMCID: PMC10037526 DOI: 10.14218/jcth.2022.00309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/30/2022] [Accepted: 11/01/2022] [Indexed: 03/29/2023] Open
Abstract
Background and Aims The prevalence of chronic liver disease in adults exceeds 30% in some countries and there is significant interest in developing tests and treatments to help control disease progression and reduce healthcare burden. Breath is a rich sampling matrix that offers non-invasive solutions suitable for early-stage detection and disease monitoring. Having previously investigated targeted analysis of a single biomarker, here we investigated a multiparametric approach to breath testing that would provide more robust and reliable results for clinical use. Methods To identify candidate biomarkers we compared 46 breath samples from cirrhosis patients and 42 from controls. Collection and analysis used Breath Biopsy OMNI™, maximizing signal and contrast to background to provide high confidence biomarker detection based upon gas chromatography mass spectrometry (GC-MS). Blank samples were also analyzed to provide detailed information on background volatile organic compounds (VOCs) levels. Results A set of 29 breath VOCs differed significantly between cirrhosis and controls. A classification model based on these VOCs had an area under the curve (AUC) of 0.95±0.04 in cross-validated test sets. The seven best performing VOCs were sufficient to maximize classification performance. A subset of 11 VOCs was correlated with blood metrics of liver function (bilirubin, albumin, prothrombin time) and separated patients by cirrhosis severity using principal component analysis. Conclusions A set of seven VOCs consisting of previously reported and novel candidates show promise as a panel for liver disease detection and monitoring, showing correlation to disease severity and serum biomarkers at late stage.
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Affiliation(s)
| | | | | | | | | | | | - Irene Debiram-Beecham
- Department of Oncology, University of Cambridge, Hutchison/MRC Research Centre, Cambridge, UK
| | | | - Graham Kibble
- Department of Oncology, University of Cambridge, Hutchison/MRC Research Centre, Cambridge, UK
| | - Anne Marie Lydon
- Department of Oncology, University of Cambridge, Hutchison/MRC Research Centre, Cambridge, UK
| | - Alice Groves
- Department of Oncology, University of Cambridge, Hutchison/MRC Research Centre, Cambridge, UK
| | - Agnieszka Smolinska
- Owlstone Medical, Cambridge, UK
- Department of Pharmacology and Toxicology, School for Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University Medical Center, the Netherlands
| | | | | | | | - Michael Allison
- Department of Medicine, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
- Addenbrookes Hepatology and Liver Transplantation Unit, Addenbrookes Hospital, Cambridge, UK
| | - Rebecca C. Fitzgerald
- MRC Cancer Unit, Hutchison/MRC Research Centre, University of Cambridge, Cambridge, UK
| | - Matthew Hoare
- Department of Medicine, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
- Addenbrookes Hepatology and Liver Transplantation Unit, Addenbrookes Hospital, Cambridge, UK
- CRUK Cambridge Institute, Cambridge, UK
| | - Victoria K. Snowdon
- Addenbrookes Hepatology and Liver Transplantation Unit, Addenbrookes Hospital, Cambridge, UK
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