| Is glycemic variability important to assessing antidiabetes therapies? | |
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MedLine Citation:
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PMID: 17076995 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Traditionally, statistical estimation of glycemic variability includes computing standard deviation of glucose readings or related statistics (eg, M value, mean amplitude of glucose excursions, and so forth). We advocate an alternative approach using risk measures of variability, which have substantial clinical and numerical advantages. In addition, continuous glucose monitoring (CGM) data have clinically important inherent temporal structure that should be taken into consideration. Thus, temporal variability methods are discussed for the analysis and interpretation of CGM output. |
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Authors:
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Boris P Kovatchev |
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Publication Detail:
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Type: Journal Article; Research Support, N.I.H., Extramural; Review |
Journal Detail:
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Title: Current diabetes reports Volume: 6 ISSN: 1534-4827 ISO Abbreviation: Curr. Diab. Rep. Publication Date: 2006 Nov |
Date Detail:
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Created Date: 2006-11-01 Completed Date: 2007-02-26 Revised Date: 2007-12-03 |
Medline Journal Info:
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Nlm Unique ID: 101093791 Medline TA: Curr Diab Rep Country: United States |
Other Details:
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Languages: eng Pagination: 350-6 Citation Subset: IM |
Affiliation:
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University of Virginia Health System, Box 800137, Charlottesville, VA 22908, USA. boris@virginia.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Blood Glucose
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analysis* Diabetes Mellitus / blood, drug therapy* Humans Hyperglycemia / blood Hypoglycemia / blood Hypoglycemic Agents / therapeutic use* |
| Grant Support | |
ID/Acronym/Agency:
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R01 DK 51562/DK/NIDDK NIH HHS |
| Chemical | |
Reg. No./Substance:
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0/Blood Glucose; 0/Hypoglycemic Agents |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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