Research graph
References from Noninvasive Interstitial Glucose Estimation Using Wearables and Machine Learning in Healthy Individuals and Individuals With Obesity: Observational Cohort Study. Local targets link to admitted publications; unresolved targets remain external evidence.
Type 2 diabetes as an inflammatory disease
10.1038/nri2925 · External reference
Prevention of type 2 diabetes through prediabetes remission without weight loss
10.1038/s41591-025-03944-9 · External reference
Continuous glucose monitoring in a healthy population: understanding the post-prandial glycemic response in individuals without diabetes mellitus
10.1016/j.metabol.2023.155640 · External reference
Impact of postprandial glycaemia on health and prevention of disease
10.1111/j.1467-789x.2012.01011.x · External reference
Personalized nutrition by prediction of glycemic responses
10.1016/j.cell.2015.11.001 · External reference
Human postprandial responses to food and potential for precision nutrition
10.1038/s41591-020-0934-0 · External reference
A digital therapeutic allowing a personalized low-glycemic nutrition for the prophylaxis of migraine: real world data from two prospective studies
10.3390/nu14142927 · External reference
10.1177/19322968241266821 · Admitted local publication
Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes
10.1089/dia.2017.0035 · External reference
Advances, challenges, and cost associated with continuous glucose monitor use in adolescents and young adults with type 1 diabetes
10.1007/s11892-021-01389-1 · External reference
Thermic effect of food in humans: methods and results from use of a respiratory chamber
10.1093/ajcn/61.5.1013 · External reference
Non-invasive measurements of blood glucose levels by time-gating mid-infrared optoacoustic signals
10.1038/s42255-024-01016-9 · External reference
In-human testing of a non-invasive continuous low-energy microwave glucose sensor with advanced machine learning capabilities
10.1016/j.bios.2023.115668 · External reference
Noninvasive glucose sensing in aqueous solutions using an active split-ring resonator
10.1109/jsen.2021.3090050 · External reference
Multimodal AI correlates of glucose spikes in people with normal glucose regulation, pre-diabetes and type 2 diabetes
10.1038/s41591-025-03849-7 · External reference
Digital biomarkers for personalized nutrition: predicting meal moments and interstitial glucose with non-invasive, wearable technologies
10.3390/nu14214465 · External reference
Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches
10.1038/s41746-021-00465-w · External reference
Digital health application integrating wearable data and behavioral patterns improves metabolic health
10.1038/s41746-023-00956-y · External reference
Digital biomarkers for interstitial glucose prediction in healthy individuals using wearables and machine learning
10.1038/s41598-025-14172-z · External reference
Accuracy of the third generation of a 14-day continuous glucose monitoring system
10.1007/s13300-023-01385-6 · External reference
Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping
10.1021/ac5025039 · External reference
Data-driven modeling and prediction of blood glucose dynamics: machine learning applications in type 1 diabetes
10.1016/j.artmed.2019.07.007 · External reference
Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms
10.3390/s22093534 · External reference
979-P: leveraging machine learning and demographic insights to optimize blood glucose management on a digital health platform
10.2337/db25-979-p · External reference
10.1201/9781315181080-4
10.1201/9781315181080-4 · External reference
10.1109/ic_aset61847.2024.10596176
10.1109/ic_aset61847.2024.10596176 · External reference
Adaptive linear step-up procedures that control the false discovery rate
10.1093/biomet/93.3.491 · External reference
Unresolved reference
External reference
Evaluating clinical accuracy of systems for self-monitoring of blood glucose
10.2337/diacare.10.5.622 · External reference
Estimating blood glucose levels using machine learning models with non-invasive wearable device data
10.3233/shti230484 · External reference
Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML
10.1038/s41598-024-84265-8 · External reference
Noninvasive continuous glucose monitoring using a multisensor-based glucometer and time series analysis
10.1038/s41598-017-13018-7 · External reference
Serum metabolites associated with increased insulin resistance and low cardiorespiratory fitness in overweight adolescents
10.1016/j.numecd.2021.09.024 · External reference
The clinical importance of visceral adiposity: a critical review of methods for visceral adipose tissue analysis
10.1259/bjr/38447238 · External reference
Serum amino acid profiles and risk of type 2 diabetes among Japanese adults in the Hitachi Health Study
10.1038/s41598-019-43431-z · External reference
Type 2 diabetes mellitus in adults: pathogenesis, prevention and therapy
10.1038/s41392-024-01951-9 · External reference
The apoB/apoA-I ratio: a strong, new risk factor for cardiovascular disease and a target for lipid-lowering therapy--a review of the evidence
10.1111/j.1365-2796.2006.01643.x · External reference
High-density lipoprotein (HDL) functionality and its relevance to atherosclerotic cardiovascular disease
10.7573/dic.212525 · External reference
Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)
10.1038/s41746-020-0260-4 · External reference
NHITS: Neural Hierarchical Interpolation for Time Series Forecasting
10.1609/aaai.v37i6.25854 · External reference
Wearable-based human flow experience recognition enhanced by transfer learning methods using emotion data
10.1016/j.compbiomed.2023.107489 · External reference
Federated learning: challenges, methods, and future directions
10.1109/msp.2020.2975749 · External reference
