Estimation and SVM classification of glucose-insulin model parameters from OGTT data: a comparison with the ADA criteria

Vargas, Paola E.; Moreles, Miguel Ángel; Peña, José Antonio de la; Monroy, Adriana; Alavez, Silvestre

doi:10.1007/s13410-020-00851-2

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…This paper, for the first time, proposed a glucose-based model for the successful extraction of useful physiological information on glucose metabolism in subjects with NGT, thereby overcoming the weaknesses of existing GOM approaches. [8][9][10][11][12][13] The model's independence from insulin measurements and exclusive use of easily accessible data enable further developments and its potential application in research and clinical practice to a large number of subjects. In particular, the proposed model could allow a more sophisticated physiological interpretation of CGM profiles collected under ambulatory conditions.…”

Section: Discussionmentioning

confidence: 99%

“…The GOM proposed in this work is based on the following generalised formulation of the OMM 13 (11) 13 (11) The meal composition is given in percentage of calories contained in the respective macronutrient content. Data are given as mean ± standard error.…”

Section: Model Formulationmentioning

confidence: 99%

“…While this significantly limits their physiological interpretation, these GOMs have been shown to contain parameters that are dependent on glucose tolerance. [8][9][10][11] Other GOMs are based on physiological principles, but can only roughly approximate the postprandial glucose dynamics and have been applied to a very limited number of subjects. 12,13 The main weakness of all mentioned GOMs, however, is that their results have not been validated against the results of a model known to provide accurate physiological information.…”

Section: Introductionmentioning

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: 99%

Section: Model Formulationmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Specifically, the distribution obtained is an Erlang distribution, which is a gamma distribution with a shape integer parameter [15]. Bayesian approaches for estimate parameters of compartmental models were proposed in [13,16,17]. Authors in [17] proposed a minimal model for glucose-insulin dynamics.…”

Section: Related Workmentioning

confidence: 99%

“…Bayesian approaches for estimate parameters of compartmental models were proposed in [13,16,17]. Authors in [17] proposed a minimal model for glucose-insulin dynamics. Their classification parameters have not biological meaning.…”

Section: Related Workmentioning

confidence: 99%

Bayesian Analysis of Glucose Dynamics during the Oral Glucose Tolerance Test (OGTT)

Flores-Arguedas¹,

Capistrán²

2020

Preprint

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In this paper, we propose a model of the dynamics of the blood glucose level during an Oral Glucose Tolerance Test (OGTT). This dynamic includes the action of insulin and glucagon in the glucose homeostasis process as the reaction of an oral stimulus. We propose a Bayesian approach in the inference of five parameters related to insulin secretion, glucagon secretion, gastrointestinal emptying, and basal glucose level. Two insulin indicators related to the glucose level in blood and in the gastrointestinal tract allow us to suggest a classification for patients with impaired insulin sensitivity.

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Antidiabetic Effect of Chinese yam (Dioscorea opposita Thunb.) in High‐Fat Diet/Streptozotocin‐Induced Diabetic Rats

Zong

Guan

et al. 2023

Starch Stärke

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This study aims to assess the effect of different doses of Chinese yam (CY) on a high‐fat diet (HFD) and a streptozotocin‐induced diabetic rat model, using resistant starch (RS) as a positive control. Rats in the normal control and diabetes model (DM) groups are fed the standard diet and HFD, respectively. The RS and CY‐intervention groups are fed with a special HFD, in which starch, maltodextrin, and sucrose in the HFD are replaced completely with RS or CY powder in different proportions: 33.33% (1/3), 66.67% (2/3), and 100% respectively. The results show that CY powder can decrease serum levels of total triglycerides, total cholesterol, low‐density lipoprotein cholesterol, fasting blood glucose, and glycated hemoglobin, lower abdominal fat weight and abdominal fat index, increase levels of insulin and hepatic glycogen, and improve glucose tolerance and insulin sensitivity in diabetic rats. In addition, CY reduces oxidative stress and inflammation and reduces spleen weight and spleen index in diabetic rats. Notably, CY shows higher efficacy than RS in glucose tolerance and serum insulin and nitric oxide levels. This study suggests that CY powder may replace RS as a functional food in diabetic diets.

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Estimation and SVM classification of glucose-insulin model parameters from OGTT data: a comparison with the ADA criteria

Cited by 5 publications

References 17 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

Bayesian Analysis of Glucose Dynamics during the Oral Glucose Tolerance Test (OGTT)

Antidiabetic Effect of Chinese yam (Dioscorea opposita Thunb.) in High‐Fat Diet/Streptozotocin‐Induced Diabetic Rats

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