Monte Carlo analysis of a new model-based method for insulin sensitivity testing

  • Authors:

  • Thomas F. Lotz;J.Geoffrey Chase;Kirsten A. McAuley;Geoffrey M. Shaw;Xing-Wei Wong;Jessica Lin;Aaron LeCompte;Christopher E. Hann;Jim I. Mann

  • Affiliations:

  • Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand;Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand;Edgar National Centre for Diabetes Research, University of Otago, Dunedin, New Zealand;Christchurch School of Medicine and Health Science, University of Otago, Christchurch, New Zealand;Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand;Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand;Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand;Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand;Edgar National Centre for Diabetes Research, University of Otago, Dunedin, New Zealand

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2008

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Abstract

Insulin resistance (IR), or low insulin sensitivity, is a major risk factor in the pathogenesis of type 2 diabetes and cardiovascular disease. A simple, high resolution assessment of IR would enable earlier diagnosis and more accurate monitoring of intervention effects. Current assessments are either too intensive for clinical settings (Euglycaemic Clamp, IVGTT) or have too low resolution (HOMA, fasting glucose/insulin). Based on high correlation of a model-based measure of insulin sensitivity and the clamp, a novel, clinically useful test protocol is designed with: physiological dosing, short duration (