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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Vydané v:Scientific reports Ročník 15; číslo 1; s. 30164 - 19
Hlavní autori: 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
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 18.08.2025
Nature Publishing Group
Nature Portfolio
Predmet:
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
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Popis
Shrnutí: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 address these issues, we investigated machine learning (ML) approaches for glucose monitoring using data from non-invasive wearables. Our study comprised two phases involving healthy participants: The main study included two experimental sessions lasting 7–8 h with two standardized test meals, totaling over 1550 interstitial glucose (IG) measurements with CGM, and high-frequency multimodal data collected by two different non-invasive sensor devices. The follow-up study involved more than 14,400 IG measurements. Using ML approaches, correlations between glycemic measures and sensor data were assessed to estimate the feasibility of accurately predicting personalized IG alterations in real-time. An ensemble feature selection-based light gradient boosting machine (LightGBM) algorithm, omitting the need for food logs, was developed. This algorithm achieved a root mean squared error (RMSE) of 18.49 ± 0.1 mg/dL and a mean absolute percentage error (MAPE) of 15.58 ± 0.09%, demonstrating the feasibility of non-invasive glucose monitoring with high accuracy, which paves the way for novel approaches in the objective prevention of diet-related diseases.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-14172-z