Cléo Nicolier, Juri Künzler, Aritz Lizoain, Daniel Kerber, Stefanie Hossmann, Martina Rothenbühler, Markus Laimer, Lilian Witthauer
{"title":"利用气相色谱-离子迁移谱仪进行呼气挥发性有机化合物分析,检测 1 型糖尿病患者的低血糖症。","authors":"Cléo Nicolier, Juri Künzler, Aritz Lizoain, Daniel Kerber, Stefanie Hossmann, Martina Rothenbühler, Markus Laimer, Lilian Witthauer","doi":"10.1111/dom.15944","DOIUrl":null,"url":null,"abstract":"<p><strong>Aim: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":158,"journal":{"name":"Diabetes, Obesity & Metabolism","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection of hypoglycaemia in type 1 diabetes through breath volatile organic compound profiling using gas chromatography-ion mobility spectrometry.\",\"authors\":\"Cléo Nicolier, Juri Künzler, Aritz Lizoain, Daniel Kerber, Stefanie Hossmann, Martina Rothenbühler, Markus Laimer, Lilian Witthauer\",\"doi\":\"10.1111/dom.15944\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aim: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":158,\"journal\":{\"name\":\"Diabetes, Obesity & Metabolism\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Diabetes, Obesity & Metabolism\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/dom.15944\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diabetes, Obesity & Metabolism","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/dom.15944","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Detection of hypoglycaemia in type 1 diabetes through breath volatile organic compound profiling using gas chromatography-ion mobility spectrometry.
Aim: 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.
Materials and methods: 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.
Results: 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.
Conclusions: 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.
期刊介绍:
Diabetes, Obesity and Metabolism is primarily a journal of clinical and experimental pharmacology and therapeutics covering the interrelated areas of diabetes, obesity and metabolism. The journal prioritises high-quality original research that reports on the effects of new or existing therapies, including dietary, exercise and lifestyle (non-pharmacological) interventions, in any aspect of metabolic and endocrine disease, either in humans or animal and cellular systems. ‘Metabolism’ may relate to lipids, bone and drug metabolism, or broader aspects of endocrine dysfunction. Preclinical pharmacology, pharmacokinetic studies, meta-analyses and those addressing drug safety and tolerability are also highly suitable for publication in this journal. Original research may be published as a main paper or as a research letter.