Linked Life Data

10484360

Source:http://linkedlifedata.com/resource/pubmed/id/10484360

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3 Pt 1
pubmed:dateCreated
1999-10-28
pubmed:abstractText
The intravenous glucose tolerance test (IVGTT) single-compartment minimal model (1CMM) method has recently been shown to overestimate glucose effectiveness and underestimate insulin sensitivity. Undermodeling, i.e., use of single- instead of two-compartment description of glucose kinetics, has been advocated to explain these limitations. We describe a new two-compartment minimal model (2CMM) into which we incorporate certain available knowledge on glucose kinetics. 2CMM is numerically identified using a Bayesian approach. Twenty-two standard IVGTT (0.30 g/kg) in normal humans were analyzed. In six subjects, the clamp-based index of insulin sensitivity (ScI) was also measured. 2CMM glucose effectiveness (S2G) and insulin sensitivity (S2I) were, respectively, 60% lower (P < 0.0001) and 35% higher (P < 0.0001) than the corresponding 1CMM S1G and S1I indexes: 2.81 +/- 0.29 (SE) vs. S1G = 4.27 +/- 0.33 ml. min(-1). kg(-1) and S2I = 11.67 +/- 1.71 vs. S1I = 8.68 +/- 1.62 10(2) ml. min(-1). kg(-1) per microU/ml. S2I was not different from ScI = 12.61 +/- 2.13 10(2) ml. min(-1). kg(-1) per microU/ml (nonsignificant), whereas S1I was 60% lower (P < 0.02). In conclusion, a new 2CMM has been presented that improves the accuracy of glucose effectiveness and insulin sensitivity estimates of the classic 1CMM from a standard IVGTT in normal humans.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
0002-9513
pubmed:author
pubmed:issnType
Print
pubmed:volume
277
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
E481-8
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
Minimal model SG overestimation and SI underestimation: improved accuracy by a Bayesian two-compartment model.
pubmed:affiliation
Department of Electronics and Informatics, University of Padova, 35131 Padova, Italy. cobelli@dei.unipd.it
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.