Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Diabetes Center Berne, Bern, Switzerland.
Diabetes Obes Metab. 2024 Dec;26(12):5737-5744. doi: 10.1111/dom.15944. Epub 2024 Sep 16.
To evaluate the relationship between breath volatile organic compounds (VOCs) and glycaemic states in individuals with type 1 diabetes (T1D), focusing on identifying specific VOCs as biomarkers for hypoglycaemia to offer a non-invasive diabetes-monitoring method.
Ten individuals with T1D underwent induced hypoglycaemia in a clinical setting. Breath samples, collected every 10-15 minutes, were analysed using gas chromatography-ion mobility spectrometry (GC-IMS). Correlation analysis and machine learning models, including Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine classifiers, were used to classify glycaemic states based on VOC profiles.
Statistical analysis revealed moderate correlations between specific VOCs (e.g. isoprene, acetone) and venous blood glucose levels. Machine learning models showed high accuracy in classifying glycaemic states, with the best performance achieved by a two-class PLS-DA model showing an accuracy of 93%, sensitivity of 92% and specificity of 94%. Key biomarkers identified included isoprene, acetone, 2-butanone, methanol, ethanol, 2-propanol and 2-pentanone.
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.
评估 1 型糖尿病（T1D）个体呼出气挥发性有机化合物（VOCs）与血糖状态之间的关系，重点识别特定 VOCs 作为低血糖的生物标志物，提供一种非侵入性的糖尿病监测方法。
10 名 T1D 患者在临床环境中经历诱导性低血糖。使用气相色谱-离子迁移谱（GC-IMS）每隔 10-15 分钟分析采集的呼出气样本。基于 VOC 谱，使用相关分析和机器学习模型（包括偏最小二乘判别分析（PLS-DA）和支持向量机分类器）对血糖状态进行分类。
统计分析显示特定 VOCs（如异戊二烯、丙酮）与静脉血糖水平之间存在中等相关性。机器学习模型显示了对血糖状态进行分类的高准确性，最佳性能由双类 PLS-DA 模型实现，准确率为 93%，灵敏度为 92%，特异性为 94%。鉴定出的关键生物标志物包括异戊二烯、丙酮、2-丁酮、甲醇、乙醇、2-丙醇和 2-戊酮。
本研究表明呼出气 VOCs 有可能准确分类 T1D 个体的血糖状态。虽然确定了异戊二烯、丙酮和 2-丁酮等关键生物标志物，但分析强调了使用整体 VOC 模式而不是单个化合物的重要性，因为后者可能是多种情况的标志物。利用这些模式的机器学习模型实现了高准确性、灵敏度和特异性。这些发现表明，使用 GC-IMS 的呼出气分析可能是一种可行的非侵入性方法，可用于监测血糖状态和管理糖尿病。
