| Independent cohort cross-validation of the real-time DISTq estimation of insulin sensitivity. | |
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MedLine Citation:
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PMID: 20800314 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Insulin sensitivity (SI) is useful in the diagnosis, screening and treatment of diabetes. However, most current tests cannot provide an accurate, immediate or real-time estimate. The DISTq method does not require insulin or C-peptide assays like most SI tests, thus enabling real-time, low-cost SI estimation. The method uses a posteriori parameter estimations in the absence of insulin or C-peptide assays to simulate accurate, patient-specific, insulin concentrations that enable SI identification. Mathematical functions for the a posteriori parameter estimates were generated using data from 46 fully sampled DIST tests (glucose, insulin and C-peptide). SI values found using the DISTq from the 46 test pilot cohort and a second independent 218 test cohort correlated R=0.890 and R=0.825, respectively, to the fully sampled (including insulin and C-peptide assays) DIST SI metrics. When the a posteriori insulin estimation functions were derived using the second cohort, correlations for the pilot and second cohorts reduced to 0.765 and 0.818, respectively. These results show accurate SI estimation is possible in the absence of insulin or C-peptide assays using the proposed method. Such estimates may only need to be generated once and then used repeatedly in the future for isolated cohorts. The reduced correlation using the second cohort was due to this cohort's bias towards low SI insulin resistant subjects, limiting the data set's ability to generalise over a wider range. All the correlations remain high enough for the DISTq to be a useful test for a number of clinical applications. The unique real-time results can be generated within minutes of testing as no insulin and C-peptide assays are required and may enable new clinical applications. |
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Authors:
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Paul D Docherty; J Geoffrey Chase; Thomas F Lotz; Christopher E Hann; Geoffrey M Shaw; Juliet E Berkeley; Lisa Temorenga; Jim I Mann; Kirsten McAuley |
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Publication Detail:
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Type: Journal Article Date: 2010-08-25 |
Journal Detail:
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Title: Computer methods and programs in biomedicine Volume: 102 ISSN: 1872-7565 ISO Abbreviation: Comput Methods Programs Biomed Publication Date: 2011 May |
Date Detail:
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Created Date: 2011-04-28 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8506513 Medline TA: Comput Methods Programs Biomed Country: Ireland |
Other Details:
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Languages: eng Pagination: 94-104 Citation Subset: IM |
Copyright Information:
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Copyright © 2010 Elsevier Ireland Ltd. All rights reserved. |
Affiliation:
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Department of Mechanical Engineering, University of Canterbury, New Zealand. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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