Prediction of glucose tolerance without an oral glucose tolerance test

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Babbar, Rohit
dc.contributor.author Heni, Martin
dc.contributor.author Peter, Andreas
dc.contributor.author de Angelis, Martin Hrabě
dc.contributor.author Häring, Hans Ulrich
dc.contributor.author Fritsche, Andreas
dc.contributor.author Preissl, Hubert
dc.contributor.author Schölkopf, Bernhard
dc.contributor.author Wagner, Róbert
dc.date.accessioned 2018-10-02T11:31:40Z
dc.date.available 2018-10-02T11:31:40Z
dc.date.issued 2018-03-19
dc.identifier.citation Babbar , R , Heni , M , Peter , A , de Angelis , M H , Häring , H U , Fritsche , A , Preissl , H , Schölkopf , B & Wagner , R 2018 , ' Prediction of glucose tolerance without an oral glucose tolerance test ' FRONTIERS IN ENDOCRINOLOGY , vol 9 , no. MAR , 82 . DOI: 10.3389/fendo.2018.00082 en
dc.identifier.issn 1664-2392
dc.identifier.other PURE UUID: afae4ccd-c8ac-4bdd-8a91-40ec27bd1ddd
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/prediction-of-glucose-tolerance-without-an-oral-glucose-tolerance-test(afae4ccd-c8ac-4bdd-8a91-40ec27bd1ddd).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85044422034&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/28055557/fendo_09_00082.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/34132
dc.description.abstract Introduction: Impaired glucose tolerance (IGT) is diagnosed by a standardized oral glucose tolerance test (OGTT). However, the OGTT is laborious, and when not performed, glucose tolerance cannot be determined from fasting samples retrospectively. We tested if glucose tolerance status is reasonably predictable from a combination of demographic, anthropometric, and laboratory data assessed at one time point in a fasting state. Methods: Given a set of 22 variables selected upon clinical feasibility such as sex, age, height, weight, waist circumference, blood pressure, fasting glucose, HbA1c, hemoglobin, mean corpuscular volume, serum potassium, fasting levels of insulin, C-peptide, triglyceride, non-esterified fatty acids (NEFA), proinsulin, prolactin, cholesterol, low-density lipoprotein, HDL, uric acid, liver transaminases, and ferritin, we used supervised machine learning to estimate glucose tolerance status in 2,337 participants of the TUEF study who were recruited before 2012. We tested the performance of 10 different machine learning classifiers on data from 929 participants in the test set who were recruited after 2012. In addition, reproducibility of IGT was analyzed in 78 participants who had 2 repeated OGTTs within 1 year. Results: The most accurate prediction of IGT was reached with the recursive partitioning method (accuracy = 0.78). For all classifiers, mean accuracy was 0.73 ± 0.04. The most important model variable was fasting glucose in all models. Using mean variable importance across all models, fasting glucose was followed by NEFA, triglycerides, HbA1c, and C-peptide. The accuracy of predicting IGT from a previous OGTT was 0.77. Conclusion: Machine learning methods yield moderate accuracy in predicting glucose tolerance from a wide set of clinical and laboratory variables. A substitution of OGTT does not currently seem to be feasible. An important constraint could be the limited reproducibility of glucose tolerance status during a subsequent OGTT. en
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries FRONTIERS IN ENDOCRINOLOGY en
dc.relation.ispartofseries Volume 9, issue MAR en
dc.rights openAccess en
dc.subject.other Endocrinology, Diabetes and Metabolism en
dc.subject.other 113 Computer and information sciences en
dc.title Prediction of glucose tolerance without an oral glucose tolerance test en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.contributor.department University of Tübingen
dc.contributor.department German Center for Diabetes Research
dc.contributor.department Max Planck Institute for Intelligent Systems
dc.subject.keyword Classification
dc.subject.keyword Clinical study
dc.subject.keyword Impaired glucose tolerance
dc.subject.keyword Machine learning classification
dc.subject.keyword Oral glucose tolerance test
dc.subject.keyword Prediction
dc.subject.keyword Supervised machine learning
dc.subject.keyword Test-retest variability
dc.subject.keyword Endocrinology, Diabetes and Metabolism
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201810025215
dc.identifier.doi 10.3389/fendo.2018.00082
dc.type.version publishedVersion


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