The Impact of Obesity on Predisposed People to Type 2 Diabetes: Mathematical Model

Boutayeb, Wiam; Derouich, Mohamed; Boutayeb, Abdesslam; Derouich, Mohammed

doi:10.1007/978-3-319-16483-0_59

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…The model, as shown below, is extended upon the model of Boutayeb et al. ( 53 ), including an equation of GH to depict the interaction of GH withglucose and free fatty acids (FFA):…”

Section: Existing Models Of Secondary Diabetesmentioning

confidence: 99%

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The progression of secondary diabetes: A review of modeling studies

Yang

Haller

et al. 2022

Front. Endocrinol.

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Mathematical modeling has provided quantitative information consistent with experimental data, greatly improving our understanding of the progression of type 1 and type 2 diabetes. However, diabetes is a complex metabolic disease and has been found to be involved in crosstalk interactions with diverse endocrine diseases. Mathematical models have also been developed to investigate the quantitative impact of various hormonal disorders on glucose imbalance, advancing the precision treatment for secondary diabetes. Here we review the models established for the study of dysglycemia induced by hormonal disorders, such as excessive glucocorticoids, epinephrine, and growth hormone. To investigate the influence of hyperthyroidism on the glucose regulatory system, we also propose a hyperthyroid-diabetes progression model. Model simulations indicate that timely thyroid treatment can halt the progression of hyperglycemia and prevent beta-cell failure. This highlights the diagnosis of hormonal disorders, together withblood sugar tests, as significant measures for the early diagnosis and treatment of diabetes. The work recapitulates updated biological research on the interactions between the glucose regulatory system and other endocrine axes. Further mathematical modeling of secondary diabetes is desired to promote the quantitative study of the disease and the development of individualized diabetic therapies.

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“…The model, as shown below, is extended upon the model of Boutayeb et al. ( 53 ), including an equation of GH to depict the interaction of GH withglucose and free fatty acids (FFA):…”

Section: Existing Models Of Secondary Diabetesmentioning

confidence: 99%

“…This work provides the first mathematical model that incorporates GH into the glucose regulatory system. The receptor equation obtained from the base model in ( 53 ) impedes this model from data fitting and applications. In addition, the insulin equation cited from the base model may be questionable.…”

Section: Existing Models Of Secondary Diabetesmentioning

confidence: 99%

The progression of secondary diabetes: A review of modeling studies

Yang

Haller

et al. 2022

Front. Endocrinol.

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“…Model comparison and selection play important roles in identifying the best model from a set of candidate models for data-driven modelling and system identification problems [ 23 ]. Existing diabetes models for type 1 and type 2 include

-cells [ 3 , 8 , 11 , 18 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. In this study, we adapted two mathematical models from our previous work, one with

-cells [ 13 ] and one without the

-cell component [ 3 ], to determine their capabilities in predicting blood glucose concentration levels and identifying type 1 diabetes pathways using published experimental data from mice studies [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Examining Type 1 Diabetes Mathematical Models Using Experimental Data

Ali

Daneshkhah

Boutayeb

et al. 2022

IJERPH

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Type 1 diabetes requires treatment with insulin injections and monitoring glucose levels in affected individuals. We explored the utility of two mathematical models in predicting glucose concentration levels in type 1 diabetic mice and determined disease pathways. We adapted two mathematical models, one with β-cells and the other with no β-cell component to determine their capability in predicting glucose concentration and determine type 1 diabetes pathways using published glucose concentration data for four groups of experimental mice. The groups of mice were numbered Mice Group 1–4, depending on the diabetes severity of each group, with severity increasing from group 1–4. A Markov Chain Monte Carlo method based on a Bayesian framework was used to fit the model to determine the best model structure. Akaike information criteria (AIC) and Bayesian information criteria (BIC) approaches were used to assess the best model structure for type 1 diabetes. In fitting the model with no β-cells to glucose level data, we varied insulin absorption rate and insulin clearance rate. However, the model with β-cells required more parameters to match the data and we fitted the β-cell glucose tolerance factor, whole body insulin clearance rate, glucose production rate, and glucose clearance rate. Fitting the models to the blood glucose concentration level gave the least difference in AIC of 1.2, and a difference in BIC of 0.12 for Mice Group 4. The estimated AIC and BIC values were highest for Mice Group 1 than all other mice groups. The models gave substantial differences in AIC and BIC values for Mice Groups 1–3 ranging from 2.10 to 4.05. Our results suggest that the model without β-cells provides a more suitable structure for modelling type 1 diabetes and predicting blood glucose concentration for hypoglycaemic episodes.

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“…Hovarka et al [6], Sorenson [16] and Puckett [15] modified minimal model and focused on Type-1 diabetic patients [18]. Topp et al, Roy et al, introduced mathematical models [4], which deal with Type-2 diabetes, however, these models ignore the effect of delays. Inclusion of the delay factors are useful for the better understanding of the oscillatory behavior of the insulin and glucose regulatory system.…”

Section: Introductionmentioning

confidence: 99%

A Polynomial Linear Regression Approach to Estimate Sensitive Parameters in the Novel Double Diabetes Model

Hussain

Arbab

Salahuddin

et al. 2020

ijaa

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Sensitivity analysis characterizes the changes in the model outputs due to the changes in the model parameters. In this article, we estimate the most sensitive parameters in the Novel Double Diabetes Model (NDDM) through the polynomial linear regression approach; this way we develop a direct relation between the sensitivity analysis and the paramter estimation. The NDDM has more than seventeen parameters, and estimating them simultanously is difficult. We select the most commonly used five parameters in the glucose-insulin dynamics for the sensitivity analysis. The model outputs-glucose concentrations in the plasma and the subcutaneous compartments are sensitive to the selected parameters whereas the insulin concentrations in the plasma and the subcutaneous compartment are sensitive only to the insulin transfer rate from the subcutaneous to the plasma compartment. System sensitivity of the model for the selected parameters is also in agreement with the individual sensitivities of the parameters. Consequently, we estimate the parameters which are more sensitive by the polynomial linear regression approach.

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The Impact of Obesity on Predisposed People to Type 2 Diabetes: Mathematical Model

Cited by 5 publications

References 10 publications

The progression of secondary diabetes: A review of modeling studies

The progression of secondary diabetes: A review of modeling studies

Examining Type 1 Diabetes Mathematical Models Using Experimental Data

A Polynomial Linear Regression Approach to Estimate Sensitive Parameters in the Novel Double Diabetes Model

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