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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Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 30164 - 19
Hlavní autoři:	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:	angličtina
Vydáno:	London Nature Publishing Group UK 18.08.2025 Nature Publishing Group Nature Portfolio
Témata:	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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Abstract	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.
AbstractList	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. Abstract 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. 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. 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.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.
ArticleNumber	30164
Author	Martensen, Lars Schröder, Torsten Piet, Artur Seitzer, Christian Otzen, Hans Günther, Ulrich L. Schmelter, Franziska Sina, Christian Witt, Oliver Marshall, Lisa Grzegorzek, Marcin Huang, Xinyu
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Keywords	Personalized nutrition Wearables Engineered biomarker Non-invasive CGM Interstitial glucose prediction Machine learning
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Snippet	A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in individuals.... Abstract A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in...
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SubjectTerms	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
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Title	Digital biomarkers for interstitial glucose prediction in healthy individuals using wearables and machine learning
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