Weight‐HbA1c‐insulin‐glucose model for describing disease progression of type 2 diabetes

We evaluated this idea on a population that underwent diet and exercise, as well as including an additional postprandial glucose factor and transit compartments to describe hemoglobin A1c formation

S Choy

2015

Scholarcy highlights

  • There have been a number of models describing biomarkers of diabetes, ranging from empirical to more mechanistic models. The commonly used biomarkers for diagnosis and subsequent monitoring of disease progression are fasting plasma glucose, fasting serum insulin, and glycated hemoglobin A1c
  • It has been hypothesized that the weight loss is tightly linked to improved insulin sensitivity, such that plasma glucose concentrations decrease with maintained insulin concentrations. de Winter et al. published a mechanism-based model for type 2 diabetes mellitus that describes the disease progression and treatment effects of oral antidiabetic drugs on FSI, FPG, and HbA1c
  • The following data were used in the analysis: weight, which was collected every two weeks during the run-in and titration phase, and every four weeks during the maintenance phase, FSI collected at the start of the run-in and titration phase, and twice during the maintenance phase, FPG collected from the start of the run-in until the end of the maintenance phase, and HbA1c collected from the start of the run-in phase until the end of the maintenance phase
  • We have evaluated the concept of using weight change as a driver for insulin sensitivity in a semi-mechanistic model, subsequently using changes in insulin sensitivity to describe FSI, FPG, and HbA1c in a diabetic population
  • In the WHIG model, the mechanismbased relationship between body weight change and insulin sensitivity was implemented as a linear function scaled to absolute weight change, which could be problematic if a patient had instead gained more than 10 kg in weight, as this would result in a negative insulin sensitivity
  • This was not an issue in our current study, a nonlinear function, such as an Emax function, would ensure a nonnegative insulin sensitivity when extrapolating beyond the data we used for modeling
  • To the authors’ knowledge, this was the first thorough study in which weight change was implemented in a semi-mechanistic model to quantify its effects on insulin sensitivity to predict the changes of fasting plasma glucose, fasting serum insulin, and hemoglobin A1c in humans with type 2 diabetes mellitus

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