Dokument: Diabetes subtyping for precision medicine – From practical implementation to methodological challenges
| Titel: | Diabetes subtyping for precision medicine – From practical implementation to methodological challenges | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=71595 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20251210-140100-4 | |||||||
| Kollektion: | Dissertationen | |||||||
| Sprache: | Englisch | |||||||
| Dokumententyp: | Wissenschaftliche Abschlussarbeiten » Dissertation | |||||||
| Medientyp: | Text | |||||||
| Autor: | Mori, Tim [Autor] | |||||||
| Dateien: |
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| Beitragende: | Kuß, Oliver [Gutachter] Hoyer, Annika [Gutachter] | |||||||
| Dewey Dezimal-Klassifikation: | 600 Technik, Medizin, angewandte Wissenschaften » 610 Medizin und Gesundheit | |||||||
| Beschreibung: | There is growing interest in precision medicine to optimise the treatment of people living with diabetes. Recent research has focused on complementing the classification into type 1 and type 2 diabetes with a more nuanced subtyping approach. In particular, it has been proposed to subdivide type 2 diabetes into mild (age-related, obesity-related) and severe (insulin-deficient, insulin-resistant) subtypes.
However, no software tools are currently available to support this reclassification of diabetes in routine clinical care. Moreover, some individuals may not be clearly assignable to any of the proposed subtypes, an issue that has not yet been systematically investigated. The aim of this work was therefore to (i) develop a user-friendly software implementation to enable diabetes subtyping in clinical practice, and (ii) propose a novel method to quantify classification uncertainty in type 2 diabetes subtypes. It was shown that (i) the newly developed DDZ Diabetes-Cluster-Tool provides clinicians with a practical means of assigning individuals to diabetes subtypes, and (ii) the normalised relative entropy provides a measure to quantify classification certainty on a scale from 0 (complete uncertainty) to 1 (complete certainty). Among individuals with newly diagnosed type 2 diabetes from the German Diabetes Study, the median normalised relative entropy was 0.127 (95% confidence interval: 0.119, 0.135), indicating substantial classification uncertainty. In conclusion, the DDZ Diabetes-Cluster-Tool is the first user-friendly software solution to enable diabetes subtyping in routine clinical care. At the same time, the results suggest that the proposed reclassification may offer only limited value for precision medicine due to uncertainty in subtype assignments. | |||||||
| Lizenz: |  Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz | |||||||
| Fachbereich / Einrichtung: | Sonstige Einrichtungen/Externe » An-Institute » Deutsches Diabetes-Zentrum | |||||||
| Dokument erstellt am: | 10.12.2025 | |||||||
| Dateien geändert am: | 10.12.2025 | |||||||
| Promotionsantrag am: | 12.08.2025 | |||||||
| Datum der Promotion: | 18.11.2025 |
