{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T17:13:14Z","timestamp":1792084394238,"version":"4.4.0"},"reference-count":42,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T00:00:00Z","timestamp":1726444800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009068","name":"University of Bern","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009068","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dom-pubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Obesity Metabolism"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>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\u2010invasive diabetes\u2010monitoring method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>Ten individuals with T1D underwent induced hypoglycaemia in a clinical setting. Breath samples, collected every 10\u201015\u2009minutes, were analysed using gas chromatography\u2013ion mobility spectrometry (GC\u2010IMS). Correlation analysis and machine learning models, including Partial Least Squares Discriminant Analysis (PLS\u2010DA) and Support Vector Machine classifiers, were used to classify glycaemic states based on VOC profiles.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2010class PLS\u2010DA model showing an accuracy of 93%, sensitivity of 92% and specificity of 94%. Key biomarkers identified included isoprene, acetone, 2\u2010butanone, methanol, ethanol, 2\u2010propanol and 2\u2010pentanone.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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\u2010butanone 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\u2010IMS could be a viable non\u2010invasive method for monitoring glycaemic states and managing diabetes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.15944","type":"journal-article","created":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T14:35:55Z","timestamp":1726583755000},"page":"5737-5744","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Detection of hypoglycaemia in type 1 diabetes through breath volatile organic compound profiling using gas chromatography\u2013ion mobility spectrometry"],"prefix":"10.1111","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1816-5156","authenticated-orcid":false,"given":"Cl\u00e9o","family":"Nicolier","sequence":"first","affiliation":[{"name":"Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital Bern University Hospital, University of Bern  Bern 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