Linked Life Data

16478778

Source:http://linkedlifedata.com/resource/pubmed/id/16478778

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2006-6-8
pubmed:abstractText
Minimal model analysis of intravenous glucose tolerance test (IVGTT) glucose and insulin concentrations offers a validated approach to measuring insulin sensitivity, but model identification is not always successful. Improvements may be achieved by using alternative settings in the modeling process, although results may differ according to setting, and care must be exercised in combining results. IVGTT data (12 samples, regular test) from 533 men without diabetes was modeled by the traditional nonlinear regression (NLR) approach, using five different permutations of settings. Results were evaluated with reference to the more robust Bayesian hierarchical (BH) approach to model identification and to the proportion of variance they explained in known correlates of insulin sensitivity (age, BMI, blood pressure, fasting glucose and insulin, serum triglyceride, HDL cholesterol, and uric acid concentration). BH analysis was successful in all cases. With NLR analysis, between 17 and 35 IVGTTs were associated with parameter coefficients of variation (PCVs) for minimal model parameters S(I) (insulin sensitivity) and S(G) (glucose effectiveness) of >100%. Systematic use of each different approach in combination reduced this number to five. Mean (interquartile range) S(I)(NLR) was then 3.14 (2.29-4.63) min(-1).mU(-1).l x 10(-4) and 2.56 (1.74-3.83) min(-1).mU(-1).l x 10(-4) for S(I)(BH) (correlation 0.86, P < 0.0001). S(I)(NLR) explained, on average, 10.6% of the variance in known correlates of insulin sensitivity, whereas S(I)(BH) explained 8.5%. In a large body of data, which BH analysis demonstrated could be fully identified, use of alternative modeling settings in NLR analysis could substantially reduce the number of analyses with PCVs >100%. S(I)(NLR) compared favorably with S(I)(BH) in the proportion of variance explained in known correlates of insulin sensitivity.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0193-1849
pubmed:author
pubmed:issnType
Print
pubmed:volume
291
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
E167-74
pubmed:dateRevised
2011-11-17
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Evaluation of nonlinear regression approaches to estimation of insulin sensitivity by the minimal model with reference to Bayesian hierarchical analysis.
pubmed:affiliation
Endocrinology and Metabolic Medicine, Imperial College London, St. Mary's Hospital, Mint Wing 2nd Floor, London W2 1PG, UK. i.godsland@imperial.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies