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    Model-Based Insulin Sensitivity and Pharmacodynamic Surfaces

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    12612098_PD Surfaces ver 2.0 - POSTER.pdf (977.6Kb)
    Author
    Chase, J.G.
    Andreassen, S.
    Pielmeier, U.
    Date
    2008
    Permanent Link
    http://hdl.handle.net/10092/2401

    Objective: The minimal model (MM) is widely used for model-based insulin sensitivity testing. A pharmacodynamic (PD) surface analysis shows how the MM can under-predict insulin sensitivity and its changes over time, particularly in high(er) insulin dose tests.

    Methods: PD surfaces at steady state are fitted to N = 77 clinical results for: 1) the MM; 2) a receptor model for type 1 diabetes (RM); and 3) an MM-derived nonlinear metabolic control model (CM). The MM has no insulin effect saturation. The CM has insulin effect saturation and glucose removal saturation can be added. The RM model saturates the combined insulin and glucose removal effect. Errors are reported as: 1) RMS; 2) Mode of the Absolute Error (AME) distribution; and 3) Frequency of Errors Near Zero (FNZ) - over all 77 reported results.

    Results: Results for the MM are: RMS = 4.77; AME = -0.05, FNZ = 3 (of 77). For the RM: RMS = 0.04; AME = -0.01, FNZ = 32. For the CM: RMS = 0.07; AME = -0.01, FNZ = 36. Adding glucose saturation effects to the CM yields: RMS = 0.06; AME = -0.01, FNZ = 39. CM and RM have small and tight error distributions.

    Conclusions: The MM consistently under-predicts insulin saturation resulting in large errors due to the shape of its PD surface. The ability to fit a single or small group of data sets can yield large error for others, illustrating the value of using a large set of clinical results to test these models. The results show that insulin and/or glucose saturation dynamics are necessary to yield consistent model-based insulin sensitivity values.

    Subjects
    Fields of Research::320000 Medical and Health Sciences::329900 Other Medical and Health Sciences::329903 Therapies and therapeutic technology
     
    Fields of Research::320000 Medical and Health Sciences::321000 Clinical Sciences::321004 Endocrinology
     
    Fields of Research::290000 Engineering and Technology::291500 Biomedical Engineering::291504 Biomechanical engineering
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    • Engineering: Conference Contributions [1816]
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