Digital biomarkers for interstitial glucose prediction in healthy individuals using wearables and machine learning

A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in individuals. However, invasiveness, high cost, and limited lifecycle of continuous glucose monitoring (CGM) devices restrict their widespread use. To addre...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 30164 - 19
Hauptverfasser:	Huang, Xinyu, Schmelter, Franziska, Seitzer, Christian, Martensen, Lars, Otzen, Hans, Piet, Artur, Witt, Oliver, Schröder, Torsten, Günther, Ulrich L., Marshall, Lisa, Grzegorzek, Marcin, Sina, Christian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 18.08.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	692/53/2423 692/700/478 Ablation Adult Algorithms Biomarkers - analysis Biomarkers - blood Blood Glucose - analysis Blood Glucose Self-Monitoring - instrumentation Blood Glucose Self-Monitoring - methods Blood levels Diabetes Diabetes mellitus Diet Engineered biomarker Feature selection Female Glucose Glucose - analysis Glucose - metabolism Glucose monitoring Healthy Volunteers Humanities and Social Sciences Humans Hypoglycemia Interstitial glucose prediction Invasiveness Learning algorithms Machine Learning Male Metabolism Middle Aged multidisciplinary Non-invasive CGM Nutrient deficiency Personalized nutrition Science Science (multidisciplinary) Sensors Wearable Electronic Devices Wearables Young Adult Personalized nutrition Wearables Engineered biomarker Non-invasive CGM Interstitial glucose prediction Machine learning
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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