Bedside analysis of volatile organic compounds in exhaled human breath as non-invasive diagnostic tool for colon diseases.

Background

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.

Aim

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.

Methods

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.

Results

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).

Conclusion

DMS-based breath analysis combined with machine learning appears promising noninvasive diagnostic approach in patients with colon diseases.