Detection of hypoglycaemia in type 1 diabetes through breath volatile organic compound profiling using gas chromatography–ion mobility spectrometry
Authors' conclusion
This study shows the potential of breath VOCs to accurately classify glycaemic states in individuals with T1D. While key biomarkers such as isoprene, acetone and 2-butanone were identified, the analysis emphasizes the importance of using overall VOC patterns rather than individual compounds, which can be markers for multiple conditions. Machine learning models leveraging these patterns achieved high accuracy, sensitivity and specificity. These findings suggest that breath analysis using GC-IMS could be a viable non-invasive method for monitoring glycaemic states and managing diabetes.
Study context
- Population
- Individuals with T1D undergoing induced hypoglycaemia; breath samples every 10–15 minutes
- Participants
- 10 individuals
- Comparison
- Breath VOC profiling by gas chromatography-ion mobility spectrometry (GC-IMS) for classifying glycaemic states
- Outcome
- Classification of glycaemic states (best model)
Methodological detailsinduced hypoglycaemia in a clinical setting, machine learning classification
- Induced hypoglycaemia in a clinical setting
- Machine learning classification
Performance
As reported in the abstract
| Test or model | Accuracy | Sensitivity | Specificity |
|---|---|---|---|
| Two-class Partial Least Squares Discriminant Analysis (PLS-DA) | 93% | 92% | 94% |
Two-class Partial Least Squares Discriminant Analysis (PLS-DA)
Further findings
Correlation of volatile organic compounds (VOCs) with venous blood glucose
- Moderate correlations between specific VOCs (e.g. isoprene, acetone) and venous blood glucose
Key biomarkers
- Key biomarkers: isoprene, acetone, 2-butanone, methanol, ethanol, 2-propanol and 2-pentanone
Original publication
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