Authors
Jakub Pietrzak, Monika Wiśnik-Sawka, Mirosław Maziejuk, Krzysztof Kucharczyk, Adam Przybyłkowski
Published in
Bioanalysis. Pages 1-8. Oct 12, 2026. Epub Oct 12, 2026.
Abstract
Measurements of volatile organic compounds (VOCs) in exhaled air represent a promising noninvasive biomarker modality for gastrointestinal diseases and has potential to enable point-of-care diagnostics.
To evaluate the feasibility of differential ion mobility spectrometry (DMS) combined with machine learning to distinguish patients with colon diseases from healthy controls based on VOCs analysis in breath.
Breath samples were collected from 50 participants, including patients with inflammatory bowel disease (IBD), diverticulosis and healthy controls. Exhaled VOC profiles were acquired using a bedside differential mobility spectrometry (DMS) platform and subsequently analyzed with machine learning models.
Based on machine-learning analysis there was moderate discrimination between patients with colon disease and healthy controls (best ROC-AUC 0.784). In exploratory disease-specific analyses the highest discriminative performance for ulcerative colitis (ROC-AUC 0.862) has been noted, whereas discrimination between Crohn's disease and diverticular disease was less accurate. In patient-level permutation testing it has been confirmed that the observed diagnostic performance was unlikely to have occurred by chance (permutation p = 0.005).
DMS-based breath analysis combined with machine learning appears promising noninvasive diagnostic approach in patients with colon diseases.
PMID:
42861887
Bibliographic data and abstract were imported from PubMed on 12 Oct 2026.
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