1 / 17

T Lotz 1 , J G Chase 1 , K A McAuley 2 , J Lin 1 , J Wong 1 , C E Hann 1 and S Andreassen 3

INTEGRAL-BASED IDENTIFICATION OF A PHYSIOLOGICAL INSULIN AND GLUCOSE MODEL ON EUGLYCAEMIC CLAMP AND IVGTT TRIALS. T Lotz 1 , J G Chase 1 , K A McAuley 2 , J Lin 1 , J Wong 1 , C E Hann 1 and S Andreassen 3 1 Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand

jermaine-burris
Download Presentation

T Lotz 1 , J G Chase 1 , K A McAuley 2 , J Lin 1 , J Wong 1 , C E Hann 1 and S Andreassen 3

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. INTEGRAL-BASED IDENTIFICATION OF A PHYSIOLOGICAL INSULIN AND GLUCOSE MODEL ON EUGLYCAEMIC CLAMP AND IVGTT TRIALS T Lotz1, J G Chase1, K A McAuley2, J Lin1, J Wong1, C E Hann1 and S Andreassen3 1Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand 2Edgar National Centre for Diabetes Research, University of Otago, Dunedin, New Zealand 3Centre for Model-based Medical Decision Support, Aalborg University, Denmark

  2. Why model glucose and insulin kinetics? • Glycaemic control from critically ill to diabetic individuals • Tight glycaemic control in ICU reduces mortality by up to 45% • Type 1 and insulin dependent Type 2 diabetes growing rapidly • Diagnosis of insulin resistance • Requires knowledge of glucose and insulin kinetics • Currently, diagnosis occurs ~7 years after initial occurrence • Current models not physiological, difficult to identify, or do not provide high resolution in clinical validation!

  3. ID - Goals • Physiologically accurate model identification • Higher predictive power and resolution • Simple application in a clinical setting • Simple identification without the need of complicated tests (minimal data required) • Use population parameters where possible, fit critical parameters • Computationally efficient

  4. uex PANCREAS nI PLASMA INTERSTITIAL FLUID x·uen CELLS nC diffusion nK nL KIDNEYS LIVER 2-compartment insulin kinetics model + glucose pharmacodynamics GLUCOSE I Q

  5. ID - problems • 2-exponential insulin model but 8 parameters • Physiological solution required Try to identify a priori as many parameters as possible Fit only the most critical parameters! Critical parameters: • Hepatic clearance nL • First pass extraction of endogenous insulin x (if enough resolution in data) • Insulin sensitivity SI • Insulin independent glucose clearance pG • Distribution volumes (if enough resolution in data)

  6. PANCREAS PLASMA VP INTERSTITIAL FLUID VQ uen nI nK KIDNEYS A priori ID - Similarities with C-peptide C-peptide (Van Cauter et al 1992) Equimolar secretion PANCREAS PLASMA VP INTERSTITIAL FLUID VQ x·uen CELLS nC nI Insulin nK nL KIDNEYS LIVER Additional losses

  7. A priori ID – insulin model • Distribution volumes (VP, VQ), transcapillary diffusion (nI), kidney clearance (nK) assumed to match values for C-peptide (similar molecular size, equimolar secretion) • Parameters taken from well validated population model for C-peptide kinetics (Van Cauter et al. 1992) • Saturation of hepatic clearance (αI) fixed from published literature • Clearance by the cells (nC)fixed to achieve ss-concentration gradient between the compartments (Iss/Qss=5/3) (Sjostrand et al 2005) 1 (2) key insulin parameters to be estimated, liver clearance nL(+ first pass hepatic extractionx if data available)

  8. A priori ID – glucose model • Glucose clearance saturation αG= 1/65 (from literature mean, validated in glycemic control trials) • Equilibrium glucose concentration GE= fasting glucose level • Glucose distribution volume VG= 0.19 x body weight (can be estimated if data allows) • Estimate pG, SI, (VG)

  9. Integral-based fitting method • Convex, not starting point dependent • Reduces ID to solving a set of very well known linear equations • 2 steps, first insulin, then glucose • Integrate insulin model between [t0,t1]: • I(t) estimated by interpolating between discrete data • Q(t) known from analytical solution: • Inputs u(t) known (endogenous insulin estimated from C-Peptide)

  10. Integral-based fitting method • Repeat for different time-steps [t0,t1] ... [tn-1,tn]: known known known identify identify solve

  11. Integral-based fitting method • ID glucose model – same approach as shown on insulin solve

  12. Example of result accuracy • Estimation of two parameters in insulin model, nL and x 2D error grid Identified values in 1 iteration! nL= 0.21 x= 0.3 0.3 0.21

  13. Validation on clamps • Euglycaemic clamp trials (N=146) • VG=0.19xbw • uen(t) assumed suppressed • Fitting errors within measurement noise: eG=5.9±6.6% SD; eI=6.2±6.4% SD G(t) I(t) Q(t)

  14. Validation on IVGTT • Data taken from Mari (Diabetologia 1998) • N=5 normal subjects • 22g glucose, 2.2U insulin (5min IV infusion) • Errors in area under curve: eAG=1.6%; eAI=6.7% G(t) I(t) Q(t)

  15. Clinical validation: Dose response test at low and high dosing Same subject on 2 different visits 10g glucose/ 1U insulin 20g glucose/ 2U insulin G(t) G(t) I(t) I(t) Q(t) Q(t)

  16. Conclusions • Physiological insulin kinetics model • Easy a-priori identification with C-peptide population model • Additional fitting of key parameters (1(2) for insulin, 2(3) for glucose) • Integral-based fitting method convex, accurate and not starting point dependent • Great potential for use in clinical applications

  17. Acknowledgements – Questions? Jessica Lin Jason Wong Chris Hann Geoff Shaw Geoff Chase Dominic Lee Kirsten McAuley Jim Mann Steen Andreassen

More Related