Bayesian parameter estimation in the oral minimal model of glucose dynamics from non-fasting conditions using a new function of glucose appearance

Eichenlaub, Manuel; Hattersley, John; Gannon, Mary C.; Nuttall, Frank Q.; Khovanova, N. A.

doi:10.1016/j.cmpb.2020.105911

Cited by 4 publications

(15 citation statements)

References 37 publications

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“…A very similar observation was made when Ra LN was used in conjunction with the OMM. 14 Similar to GA, the GOM's ability to infer information on insulin dynamics is demonstrated by the similarity of average profiles of Y OMM and Y GOM (Figure 7). To assess the agreement between the individual results of GA and insulin dynamics, the OL value was introduced.…”

Section: Discussionmentioning

confidence: 96%

“…The dataset used in this work was collected by Ahmed et al 18 and Nuttall et al 19 and is publically accessible. 20 It contains plasma glucose and insulin profiles from subjects with normal glucose tolerance (NGT) collected over 12 hours in a single day, where subjects consumed three identical meals four hours apart. Blood samples were collected at the same time in each subject after meal consumption at 0, 2, 5, 10, 20, 30, 40, 50, 60 min, then every 15 min up to 120 min and then every 30 min up to 240 min.…”

Section: Data Descriptionmentioning

confidence: 99%

“…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%

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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: Discussionmentioning

confidence: 96%

Section: Data Descriptionmentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

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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

show abstract

“…Details regarding the underlying mathematical analysis can be found in the respective publications. 3,4 The OMM is part of the "oral minimal model method" which has been used in a large number of clinical studies to investigate the regulation of the postprandial metabolism dependent on various factors such as age, sex, ethnicity, and glucose tolerance as well as the effect of different drugs used for diabetes treatment. 5,6 To assess the impact of the here discussed error in the modeling procedure, it is important to note that ignoring or not recognizing the structural identifiability of parameters can result in unreliable model estimates.…”

Section: Letter To the Editormentioning

confidence: 99%

“…Thus, V and S G are usually fixed to population values.” 1 While the parameter V is truly non-identifiable, in 2008 Saccomani et al 3 demonstrated that S G is uniquely identifiable by using a differential algebra method. Eichenlaub et al 4 then recently confirmed these results by combining the Taylor series method with symbolic computation, showing that conclusions drawn on the sole basis of a truncated expansion may be incorrect. Details regarding the underlying mathematical analysis can be found in the respective publications.…”

mentioning

confidence: 96%