A Minimal Model Approach for the Description of Postprandial Glucose Responses from Glucose Sensor Data in Diabetes Mellitus

Eichenlaub, Manuel; Hattersley, John; Khovanova, N. A.

doi:10.1109/embc.2019.8857195

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…The parameter estimation is carried out using a variational Bayesian approach, [27][28][29] which has been used previously to identify low dimensional models including the OMM. 10,14,[30][31][32] This approach provides a probabilistic treatment of unknown parameters which allows the estimation of parameter uncertainty and requires the specification of prior distributions over unknown parameters. All unknown parameters of the GOM are specified as log-normally distributed and characterised by their median and coefficient of variation (CV) since the parameters are only physiologically plausible when positive.…”

Section: Parameter Estimationmentioning

confidence: 99%

A Glucose-Only Model to Extract Physiological Information from Postprandial Glucose Profiles in Subjects with Normal Glucose Tolerance

Eichenlaub

Khovanova

Gannon

et al. 2021

J Diabetes Sci Technol

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Background: Current 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). Methods: The 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. Results: The 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 ( r = 0.7) Furthermore, the dynamic properties of the time profiles of GA and insulin dynamics inferred by the GOM show high similarity to the corresponding results of the OMM. Conclusions: The 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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Section: Parameter Estimationmentioning

confidence: 99%

A Glucose-Only Model to Extract Physiological Information from Postprandial Glucose Profiles in Subjects with Normal Glucose Tolerance

Eichenlaub

Khovanova

Gannon

et al. 2021

J Diabetes Sci Technol

Self Cite

View full text Add to dashboard Cite

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Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model

O’Donovan¹,

Erdős²,

Jacobs³

et al. 2022

iScience

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Leveraging continuous glucose monitoring for personalized modeling of insulin-regulated glucose metabolism

Erdős,

O’Donovan,

Adriaens

et al. 2024

Sci Rep

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Continuous glucose monitoring (CGM) is a promising, minimally invasive alternative to plasma glucose measurements for calibrating physiology-based mathematical models of insulin-regulated glucose metabolism, reducing the reliance on in-clinic measurements. However, the use of CGM glucose, particularly in combination with insulin measurements, to develop personalized models of glucose regulation remains unexplored. Here, we simultaneously measured interstitial glucose concentrations using CGM as well as plasma glucose and insulin concentrations during an oral glucose tolerance test (OGTT) in individuals with overweight or obesity to calibrate personalized models of glucose-insulin dynamics. We compared the use of interstitial glucose with plasma glucose in model calibration, and evaluated the effects on model fit, identifiability, and model parameters’ association with clinically relevant metabolic indicators. Models calibrated on both plasma and interstitial glucose resulted in good model fit, and the parameter estimates associated with metabolic indicators such as insulin sensitivity measures in both cases. Moreover, practical identifiability of model parameters was improved in models estimated on CGM glucose compared to plasma glucose. Together these results suggest that CGM glucose may be considered as a minimally invasive alternative to plasma glucose measurements in model calibration to quantify the dynamics of glucose regulation.

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A Minimal Model Approach for the Description of Postprandial Glucose Responses from Glucose Sensor Data in Diabetes Mellitus

Cited by 6 publications

References 15 publications

A Glucose-Only Model to Extract Physiological Information from Postprandial Glucose Profiles in Subjects with Normal Glucose Tolerance

A Glucose-Only Model to Extract Physiological Information from Postprandial Glucose Profiles in Subjects with Normal Glucose Tolerance

Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model

Leveraging continuous glucose monitoring for personalized modeling of insulin-regulated glucose metabolism

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