Glucose predictability, blood capillary permeability, and glucose utilization rate in subcutaneous, skeletal muscle, and visceral fat tissues

Koutný, Tomáš

doi:10.1016/j.compbiomed.2013.08.008

Cited by 13 publications

(9 citation statements)

References 23 publications

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“…Within the effective range of 0»0.2, all can be considered to be the best parameter values of this ratio. This is in agreement with other reports that the mass of glucose transferred increases with an increase in the concentration gradient [11].…”

Section: Pressure Gradient-driven Transport Ratiosupporting

confidence: 94%

“…This may be because the glucose diffusion over such a small distance is rapid and occurs within a few seconds. Our findings are in agreement with a previous study [11], in which ISF glucose concentrations in different tissues, e.g. the subcutaneous tissue, the skeletal muscle tissue and the visceral fat, were found to be approximately equal.…”

Section: Diffusion Distance Of Glucose Moleculesupporting

confidence: 94%

“…However, the difference among the ISF glucose concentrations simulated with different diffusion values was small, indicating that the diffusion coefficient has little influence on the model. This is in accordance with different tissue position with similar ISF glucose concentrations [11].…”

Section: Glucose Diffusion Coefficientsupporting

confidence: 87%

“…Barman et al [8,9] proposed pre-calibration dynamic concentration correction and post-prediction dynamic concentration correction models with one-parameter, delay coefficient reflecting all potential changes in the model. Koutny [10][11][12][13] utilized a differential method to change the mass transfer model into a new one including the parameters of glucose permeability, glucose uptake rate, blood flow etc. Our glucose microcirculation compartmental model (GMCM) proposed before [3] also introduced many parameters to reflect the glucose dynamic properties between blood and ISF, like glucose permeability, blood flow and glucose diffusion coefficient etc.…”

Section: Introductionmentioning

confidence: 99%

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Effect of physiological parameters on glucose microcirculation compartmental model in glucose monitoring

Shi

Zhang

Lu³

2017

Biotechnology & Biotechnological Equipment

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A glucose microcirculation compartmental model (GMCM) for predicting blood glucose concentrations from the reference interstitial fluid (ISF) glucose concentrations was proposed before. It was used to compensate the delay error in ISF in order to improve the prediction accuracy and to facilitate the clinical application of continuous blood glucose monitoring. In this paper, many physiological parameters in GMCM reflecting delay and transport properties were discussed in details. The significance and influence of the parameters, diffusion distance (x), pressure gradient-driven transport ratio (R Starling ), blood flow velocity (F), glucose capillary permeability (P), glucose diffusion coefficient (D), on the model were simulated and analysed. One group of blood (ISF) glucose values was set as input for the model with one varied parameter and other fixed parameters to predict ISF (blood) glucose concentrations. The results verified that F and P have much influence on the model and others play more minor roles. And some have positive influence on the model, like P, while others have a reverse effect, like F. All parameters can be modulated for different species or different physiological status during the model application.

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Section: Pressure Gradient-driven Transport Ratiosupporting

confidence: 94%

Section: Diffusion Distance Of Glucose Moleculesupporting

confidence: 94%

Section: Glucose Diffusion Coefficientsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effect of physiological parameters on glucose microcirculation compartmental model in glucose monitoring

Shi

Zhang

Lu³

2017

Biotechnology & Biotechnological Equipment

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show abstract

“…In other studies, ISF glucose changes are discussed by using a human subcutaneous tissue model into a mass transfer model formula in reference to glucose changes in the capillaries [ 7 ]. Combined mass transfer and black box-like models are developed with noninvasive methods and relied on input and output glucose values for detecting blood glucose concentrations, respectively [ 8 – 10 ]. The disadvantage of these studies is that they rely on a few unknown parameters for explaining such a complex system [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Measurement of Correlation between Blood and Interstitial Glucose Changes

Shi

et al. 2016

Journal of Diabetes Research

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One of the most effective methods for continuous blood glucose monitoring is to continuously measure glucose in the interstitial fluid (ISF). However, multiple physiological factors can modulate glucose concentrations and affect the lag phase between blood and ISF glucose changes. This study aims to develop a compensatory tool for measuring the delay in ISF glucose variations in reference to blood glucose changes. A theoretical model was developed based on biophysics and physiology of glucose transport in the microcirculation system. Blood and interstitial fluid glucose changes were measured in mice and rats by fluorescent and isotope methods, respectively. Computer simulation mimicked curves were fitted with data resulting from fluorescent measurements of mice and isotope measurements of rats, indicating that there were lag times for ISF glucose changes. It also showed that there was a required diffusion distance for glucose to travel from center of capillaries to interstitial space in both mouse and rat models. We conclude that it is feasible with the developed model to continuously monitor dynamic changes of blood glucose concentration through measuring glucose changes in ISF with high accuracy, which requires correct parameters for determining and compensating for the delay time of glucose changes in ISF.

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Accurate prediction of continuous blood glucose based on support vector regression and differential evolution algorithm

Hamdi¹,

Ali²,

Costanzo³

et al. 2018

Biocybernetics and Biomedical Engineering

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Glucose predictability, blood capillary permeability, and glucose utilization rate in subcutaneous, skeletal muscle, and visceral fat tissues

Cited by 13 publications

References 23 publications

Effect of physiological parameters on glucose microcirculation compartmental model in glucose monitoring

Effect of physiological parameters on glucose microcirculation compartmental model in glucose monitoring

Modeling and Measurement of Correlation between Blood and Interstitial Glucose Changes

Accurate prediction of continuous blood glucose based on support vector regression and differential evolution algorithm

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