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Machine learning refutes loss of smell as a risk indicator of diabetes mellitus

  • Because it is associated with central nervous changes, and olfactory dysfunction has been reported with increased prevalence among persons with diabetes, this study addressed the question of whether the risk of developing diabetes in the next 10 years is reflected in olfactory symptoms. In a cross-sectional study, in 164 individuals seeking medical consulting for possible diabetes, olfactory function was evaluated using a standardized clinical test assessing olfactory threshold, odor discrimination, and odor identification. Metabolomics parameters were assessed via blood concentrations. The individual diabetes risk was quantified according to the validated German version of the “FINDRISK” diabetes risk score. Machine learning algorithms trained with metabolomics patterns predicted low or high diabetes risk with a balanced accuracy of 63–75%. Similarly, olfactory subtest results predicted the olfactory dysfunction category with a balanced accuracy of 85–94%, occasionally reaching 100%. However, olfactory subtest results failed to improve the prediction of diabetes risk based on metabolomics data, and metabolomics data did not improve the prediction of the olfactory dysfunction category based on olfactory subtest results. Results of the present study suggest that olfactory function is not a useful predictor of diabetes.

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Metadaten
Author:Jörn LötschORCiDGND, Antje HähnerORCiDGND, Peter E. H. SchwarzORCiDGND, Sergey TselminGND, Thomas HummelORCiDGND
URN:urn:nbn:de:hebis:30:3-755667
DOI:https://doi.org/10.3390/jcm10214971
ISSN:2077-0383
Parent Title (English):Journal of Clinical Medicine
Publisher:MDPI
Place of publication:Basel
Document Type:Article
Language:English
Date of Publication (online):2021/10/26
Date of first Publication:2021/10/26
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2023/09/11
Tag:data science; diabetes mellitus; human olfaction; machine-learning; patients
Volume:10
Issue:21, art. 4971
Article Number:4971
Page Number:26
HeBIS-PPN:514232048
Institutes:Medizin
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0