Main navigation | Main content

HOME » PROGRAMS/ACTIVITIES » Annual Thematic Program

PROGRAMS/ACTIVITIES

Annual Thematic Program »Postdoctoral Fellowships »Hot Topics and Special »Public Lectures »New Directions »PI Programs »Math Modeling »Seminars »Be an Organizer »Annual »Hot Topics »PI Summer »PI Conference »Applying to Participate »

Talk Abstract

Mathematical Modeling of Insulin Action and In Vivo Estimates of Insulin Sensitivity

Mathematical Modeling of Insulin Action and In Vivo Estimates of Insulin Sensitivity

Senior Investigator

Hypertension-Endocrine Branch

NHLBI, NIH

Bethesda, MD 20892-1754

QuonM@gwgate.nhlbi.nih.gov

Diabetes, obesity, and hypertension are major inter-related
public health problems that are all characterized, in part,
by insulin resistance (decreased sensitivity or responsiveness
to metabolic actions of insulin). Therefore, it is of great
interest to develop tools to quantify insulin sensitivity in
vivo. The "gold standard" hyperinsulinemic euglycemic
glucose clamp method is labor intensive and not well suited
to large studies. A well-accepted alternative for estimating
insulin sensitivity in vivo is to analyze insulin and glucose
data from a frequently sampled intravenous glucose tolerance
test (FSIVGTT) using Bergman*s minimal model of glucose metabolism.
This is less cumbersome than the glucose clamp but still requires
at least 3 hours to complete. Estimates of insulin sensitivity
(SIMM) derived from minimal model analysis correlate well with
measurements of insulin sensitivity using the glucose clamp
technique (SIClamp). In addition, SIMM has predictive power
with respect to the development of diabetes. Nevertheless, we
have previously shown that minimal model analysis systematically
underestimates the effect of glucose on glucose disposal and
therefore overestimates SIMM (Quon et al., Diabetes 43:890-896,
1994). Furthermore, we have recently shown that this error is
due to an oversimplified single-compartment representation of
glucose kinetics and is dependent on the dynamics of insulin
secretion (Cobelli et al., Am J Phsyiol 38:E1031-E1036, 1998).
Therefore, we have developed an alternative Quantitative Insulin-sensitivity
Check Index (QUICKI). After analyzing data from both glucose
clamp and FSIVGTT studies, we discovered that physiological
steady-state values (i.e., fasting insulin (I_{0}) and
fasting glucose (G_{0})) contain important information
related to insulin sensitivity and thus defined QUICKI as 1/[log
(I_{0}) + log (G_{0})]. Correlations of QUICKI
with SIClamp were as good, or better, than correlations of SIMM
with SIClamp. We conclude that QUICKI is a simple, accurate,
and reliable insulin sensitivity index obtained from a single
fasting blood sample that may be useful for clinical research
and epidemiological studies related to diabetes and other insulin
resistant states.