Literature DB >> 24828465

Personalized mechanistic models for exercise, meal and insulin interventions in children and adolescents with type 1 diabetes.

Naviyn Prabhu Balakrishnan1, Lakshminarayanan Samavedham1, Gade Pandu Rangaiah2.   

Abstract

Personalized mechanistic models involving exercise, meal and insulin interventions for type 1 diabetic children and adolescents are not commonly seen in the literature. Patient specific variations in blood glucose homeostasis and adverse effects of exercise-induced hypoglycemia emphasize the need for personalized models. Hence, a modified mechanistic model for exercise, meal and insulin interventions is proposed and tailored as personalized models for 34 type 1 diabetic children and adolescents. This is achieved via a 3-stage methodology comprising of modification, a priori identifiability analysis, and personalized parameter estimation and validation using the clinical data. Rate of perceived exertion is introduced as a marker quantifying exercise intensity. Six out of 16 parameters in the modified model are identified to be estimable and are estimated for each subject as personalized parameters. The R(2) values for both fitness and validation vary between 0.7 and 0.96 in 97% of the patients, indicating the goodness of the proposed model in explaining the glucose dynamics. For most of the estimated parameters, values of personalized point estimates and their confidence intervals are found to be within physiological ranges reported in the modeling literature. Personalized values of appearance rate of exercise effect on glucose uptake in 34 subjects are 54-250% higher than the nominal values of adults. This is expected for children and adolescents as the literature shows that they exhibit higher fat and exogenous carbohydrate oxidation rates during exercise when compared to adults.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Blood glucose; Confidence intervals; Identifiability analysis; Ordinary differential equations; Parameter estimation

Mesh:

Substances:

Year:  2014        PMID: 24828465     DOI: 10.1016/j.jtbi.2014.04.038

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  1 in total

1.  Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients.

Authors:  Syed Mohammed Arshad Zaidi; Varun Chandola; Muhanned Ibrahim; Bianca Romanski; Lucy D Mastrandrea; Tarunraj Singh
Journal:  Sci Rep       Date:  2021-12-21       Impact factor: 4.379

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.