ArticleJournal of diabetes science and technology2022
A Glucose-Only Model to Extract Physiological Information from Postprandial Glucose Profiles in Subjects with Normal Glucose Tolerance.
Article in Journal of diabetes science and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Non-Fasting Glucose Measures and Their Clinical Significance in Diabetes Diagnosis and Cardiovascular and Cancer Risk Prediction: A Narrative Review.International journal of molecular sciences · 2026Review
- Beyond scalar metrics: functional data analysis of postprandial continuous glucose monitoring in the AEGIS study.BMC medical research methodology · 2026Article
- Postprandial 2-h glucose tolerance is associated with diabetes diagnosis, diabetes mortality, and cardiovascular mortality.Scientific reports · 2025Article
- Article
- Postprandial Plasma Glucose between 4 and 7.9 h May Be a Potential Diagnostic Marker for Diabetes.Biomedicines · 2024Article
- Postprandial Plasma Glucose Measured from Blood Taken between 4 and 7.9 h Is Positively Associated with Mortality from Hypertension and Cardiovascular Disease.Journal of cardiovascular development and disease · 2024Article
- Methods for estimating insulin resistance from untargeted metabolomics data.Metabolomics : Official journal of the Metabolomic Society · 2023Article
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Authors and funding
5 authors.
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Abstract
backgroundCurrent mathematical models of postprandial glucose metabolism in people with normal and impaired glucose tolerance rely on insulin measurements and are therefore not applicable in clinical practice. This research aims to develop a model that only requires glucose data for parameter estimation while also providing useful information on insulin sensitivity, insulin dynamics and the meal-related glucose appearance (GA).
methodsThe proposed glucose-only model (GOM) is based on the oral minimal model (OMM) of glucose dynamics and substitutes the insulin dynamics with a novel function dependant on glucose levels and GA. A Bayesian method and glucose data from 22 subjects with normal glucose tolerance are utilised for parameter estimation. To validate the results of the GOM, a comparison to the results of the OMM, obtained by using glucose and insulin data from the same subjects is carried out.
resultsThe proposed GOM describes the glucose dynamics with comparable precision to the OMM with an RMSE of 5.1 ± 2.3 mg/dL and 5.3 ± 2.4 mg/dL, respectively and contains a parameter that is significantly correlated to the insulin sensitivity estimated by the OMM (
conclusionsThe proposed GOM can be used to extract useful physiological information on glucose metabolism in subjects with normal glucose tolerance. The model can be further developed for clinical applications to patients with impaired glucose tolerance under the use of continuous glucose monitoring data.
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