Diagnosing Insulin Resistance in the General Population

Diagnosing Insulin Resistance in the General Population Kirsten A. McAuley , MBCHB 1 , Sheila M. Williams , BSC(HONS) 2 , Jim I. Mann , DM, PHD, FRACP 1 , Robert J. Walker , MD, FRACP 3 , Nick J. Lewis-Barned , FRACP 4 , Lara A. Temple , BSC 1 and Ashley W. Duncan , BSC, NZCS 1 1 Department of Human...

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Published inDiabetes care Vol. 24; no. 3; pp. 460 - 464
Main Authors McAuley, Kirsten A., Williams, Sheila M., Mann, Jim I., Walker, Robert J., Lewis-Barned, Nick J., Temple, Lara A., Duncan, Ashley W.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Alexandria, VA American Diabetes Association 01.03.2001
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Abstract Diagnosing Insulin Resistance in the General Population Kirsten A. McAuley , MBCHB 1 , Sheila M. Williams , BSC(HONS) 2 , Jim I. Mann , DM, PHD, FRACP 1 , Robert J. Walker , MD, FRACP 3 , Nick J. Lewis-Barned , FRACP 4 , Lara A. Temple , BSC 1 and Ashley W. Duncan , BSC, NZCS 1 1 Department of Human Nutrition 2 Department of Preventive and Social Medicine, and 3 Department of Medicine, Otago University, Dunedin, New Zealand 4 Department of Diabetes and Endocrinology, Gloucestershire Royal Hospital, Gloucester, U.K. Abstract OBJECTIVE —Difficulties in measuring insulin sensitivity prevent the identification of insulin-resistant individuals in the general population. Therefore, we compared fasting insulin, homeostasis model assessment (HOMA), insulin-to-glucose ratio, Bennett index, and a score based on weighted combinations of fasting insulin, BMI, and fasting triglycerides with the euglycemic insulin clamp to determine the most appropriate method for assessing insulin resistance in the general population. RESEARCH DESIGN AND METHODS —Family history of diabetes, BMI, blood pressure, waist and hip circumference, fasting lipids, glucose, insulin, liver enzymes, and insulin sensitivity index (ISI) using the euglycemic insulin clamp were obtained for 178 normoglycemic individuals aged 25–68 years. Product-moment correlations were used to examine the association between ISI and various surrogate measurements of insulin sensitivity. Regression models were used to devise weights for each variable and to identify cutoff points for individual components of the score. A bootstrap procedure was used to identify the most useful predictors of ISI. RESULTS —Correlation coefficients between ISI and fasting insulin, HOMA, insulin-to-glucose ratio, and the Bennett index were similar in magnitude. The variables that best predicted insulin sensitivity were fasting insulin and fasting triglycerides. The use of a score based on rather than the use of fasting insulin alone resulted in a higher sensitivity and a maintained specificity when predicting insulin sensitivity. CONCLUSIONS —A weighted combination of two routine laboratory measurements, i.e., fasting insulin and triglycerides, provides a simple means of screening for insulin resistance in the general population. AST, aspartate aminotransferase BP, blood pressure HOMA, homeostasis model assessment IGT, impaired glucose tolerance ISI, insulin sensitivity index IVGTT, intravenous glucose tolerance test M, glucose disposal rate Mbw/I, ISI corrected for total body weight divided by average insulin Mffm/I, ISI corrected for fat-free mass divided by average insulin PSEP, prognostic separation index TAG, triglycerides WHR, waist-to-hip ratio Footnotes Address correspondence and reprint requests to Dr. Kirsten McAuley, Department of Human Nutrition, Otago University, P.O. Box 56, Dunedin, New Zealand. E-mail: kirsten.mcauley{at}stonebow.otago.ac.nz . Received for publication 25 July 2000 and accepted in revised form 9 November 2000. N.J.L.-B. has received grants from the National Heart Foundation of New Zealand and the Laurensen Trust (Otago Medical Foundation). A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.
AbstractList Difficulties in measuring insulin sensitivity prevent the identification of insulin-resistant individuals in the general population. Therefore, we compared fasting insulin, homeostasis model assessment (HOMA), insulin-to-glucose ratio, Bennett index, and a score based on weighted combinations of fasting insulin, BMI, and fasting triglycerides with the euglycemic insulin clamp to determine the most appropriate method for assessing insulin resistance in the general population. Family history of diabetes, BMI, blood pressure, waist and hip circumference, fasting lipids, glucose, insulin, liver enzymes, and insulin sensitivity index (ISI) using the euglycemic insulin clamp were obtained for 178 normoglycemic individuals aged 25-68 years. Product-moment correlations were used to examine the association between ISI and various surrogate measurements of insulin sensitivity. Regression models were used to devise weights for each variable and to identify cutoff points for individual components of the score. A bootstrap procedure was used to identify the most useful predictors of ISI. Correlation coefficients between ISI and fasting insulin, HOMA, insulin-to-glucose ratio, and the Bennett index were similar in magnitude. The variables that best predicted insulin sensitivity were fasting insulin and fasting triglycerides. The use of a score based on Mffm/I = exp[2.63 - 0.28ln(insulin) - 0.31ln(TAG)] rather than the use of fasting insulin alone resulted in a higher sensitivity and a maintained specificity when predicting insulin sensitivity. A weighted combination of two routine laboratory measurements, i.e., fasting insulin and triglycerides, provides a simple means of screening for insulin resistance in the general population.
Difficulties in measuring insulin sensitivity prevent the identification of insulin-resistant individuals in the general population. Therefore, we compared fasting insulin, homeostasis model assessment (HOMA), insulin-to-glucose ratio, Bennett index, and a score based on weighted combinations of fasting insulin, BMI, and fasting triglycerides with the euglycemic insulin clamp to determine the most appropriate method for assessing insulin resistance in the general population. Family history of diabetes, BMI, blood pressure, waist and hip circumference, fasting lipids, glucose, insulin, liver enzymes, and insulin sensitivity index (ISI) using the euglycemic insulin clamp were obtained for 178 normoglycemic individuals aged 25-68 years. Product-moment correlations were used to examine the association between ISI and various surrogate measurements of insulin sensitivity. Regression models were used to devise weights for each variable and to identify cutoff points for individual components of the score. A bootstrap procedure was used to identify the most useful predictors of ISI. Correlation coefficients between ISI and fasting insulin, HOMA, insulin-to-glucose ratio, and the Bennett index were similar in magnitude. The variables that best predicted insulin sensitivity were fasting insulin and fasting triglycerides. The use of a score based on Mffm/I = exp[2.63 - 0.28ln(insulin) - 0.31ln(TAG)] rather than the use of fasting insulin alone resulted in a higher sensitivity and a maintained specificity when predicting insulin sensitivity. A weighted combination of two routine laboratory measurements, i.e., fasting insulin and triglycerides, provides a simple means of screening for insulin resistance in the general population.
Diagnosing Insulin Resistance in the General Population Kirsten A. McAuley , MBCHB 1 , Sheila M. Williams , BSC(HONS) 2 , Jim I. Mann , DM, PHD, FRACP 1 , Robert J. Walker , MD, FRACP 3 , Nick J. Lewis-Barned , FRACP 4 , Lara A. Temple , BSC 1 and Ashley W. Duncan , BSC, NZCS 1 1 Department of Human Nutrition 2 Department of Preventive and Social Medicine, and 3 Department of Medicine, Otago University, Dunedin, New Zealand 4 Department of Diabetes and Endocrinology, Gloucestershire Royal Hospital, Gloucester, U.K. Abstract OBJECTIVE —Difficulties in measuring insulin sensitivity prevent the identification of insulin-resistant individuals in the general population. Therefore, we compared fasting insulin, homeostasis model assessment (HOMA), insulin-to-glucose ratio, Bennett index, and a score based on weighted combinations of fasting insulin, BMI, and fasting triglycerides with the euglycemic insulin clamp to determine the most appropriate method for assessing insulin resistance in the general population. RESEARCH DESIGN AND METHODS —Family history of diabetes, BMI, blood pressure, waist and hip circumference, fasting lipids, glucose, insulin, liver enzymes, and insulin sensitivity index (ISI) using the euglycemic insulin clamp were obtained for 178 normoglycemic individuals aged 25–68 years. Product-moment correlations were used to examine the association between ISI and various surrogate measurements of insulin sensitivity. Regression models were used to devise weights for each variable and to identify cutoff points for individual components of the score. A bootstrap procedure was used to identify the most useful predictors of ISI. RESULTS —Correlation coefficients between ISI and fasting insulin, HOMA, insulin-to-glucose ratio, and the Bennett index were similar in magnitude. The variables that best predicted insulin sensitivity were fasting insulin and fasting triglycerides. The use of a score based on rather than the use of fasting insulin alone resulted in a higher sensitivity and a maintained specificity when predicting insulin sensitivity. CONCLUSIONS —A weighted combination of two routine laboratory measurements, i.e., fasting insulin and triglycerides, provides a simple means of screening for insulin resistance in the general population. AST, aspartate aminotransferase BP, blood pressure HOMA, homeostasis model assessment IGT, impaired glucose tolerance ISI, insulin sensitivity index IVGTT, intravenous glucose tolerance test M, glucose disposal rate Mbw/I, ISI corrected for total body weight divided by average insulin Mffm/I, ISI corrected for fat-free mass divided by average insulin PSEP, prognostic separation index TAG, triglycerides WHR, waist-to-hip ratio Footnotes Address correspondence and reprint requests to Dr. Kirsten McAuley, Department of Human Nutrition, Otago University, P.O. Box 56, Dunedin, New Zealand. E-mail: kirsten.mcauley{at}stonebow.otago.ac.nz . Received for publication 25 July 2000 and accepted in revised form 9 November 2000. N.J.L.-B. has received grants from the National Heart Foundation of New Zealand and the Laurensen Trust (Otago Medical Foundation). A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.
Difficulties in measuring insulin sensitivity prevent the identification of insulin-resistant individuals in the general population. Therefore, we compared fasting insulin, homeostasis model assessment (HOMA), insulin-to-glucose ratio, Bennett index, and a score based on weighted combinations of fasting insulin, BMI, and fasting triglycerides with the euglycemic insulin clamp to determine the most appropriate method for assessing insulin resistance in the general population.OBJECTIVEDifficulties in measuring insulin sensitivity prevent the identification of insulin-resistant individuals in the general population. Therefore, we compared fasting insulin, homeostasis model assessment (HOMA), insulin-to-glucose ratio, Bennett index, and a score based on weighted combinations of fasting insulin, BMI, and fasting triglycerides with the euglycemic insulin clamp to determine the most appropriate method for assessing insulin resistance in the general population.Family history of diabetes, BMI, blood pressure, waist and hip circumference, fasting lipids, glucose, insulin, liver enzymes, and insulin sensitivity index (ISI) using the euglycemic insulin clamp were obtained for 178 normoglycemic individuals aged 25-68 years. Product-moment correlations were used to examine the association between ISI and various surrogate measurements of insulin sensitivity. Regression models were used to devise weights for each variable and to identify cutoff points for individual components of the score. A bootstrap procedure was used to identify the most useful predictors of ISI.RESEARCH DESIGN AND METHODSFamily history of diabetes, BMI, blood pressure, waist and hip circumference, fasting lipids, glucose, insulin, liver enzymes, and insulin sensitivity index (ISI) using the euglycemic insulin clamp were obtained for 178 normoglycemic individuals aged 25-68 years. Product-moment correlations were used to examine the association between ISI and various surrogate measurements of insulin sensitivity. Regression models were used to devise weights for each variable and to identify cutoff points for individual components of the score. A bootstrap procedure was used to identify the most useful predictors of ISI.Correlation coefficients between ISI and fasting insulin, HOMA, insulin-to-glucose ratio, and the Bennett index were similar in magnitude. The variables that best predicted insulin sensitivity were fasting insulin and fasting triglycerides. The use of a score based on Mffm/I = exp[2.63 - 0.28ln(insulin) - 0.31ln(TAG)] rather than the use of fasting insulin alone resulted in a higher sensitivity and a maintained specificity when predicting insulin sensitivity.RESULTSCorrelation coefficients between ISI and fasting insulin, HOMA, insulin-to-glucose ratio, and the Bennett index were similar in magnitude. The variables that best predicted insulin sensitivity were fasting insulin and fasting triglycerides. The use of a score based on Mffm/I = exp[2.63 - 0.28ln(insulin) - 0.31ln(TAG)] rather than the use of fasting insulin alone resulted in a higher sensitivity and a maintained specificity when predicting insulin sensitivity.A weighted combination of two routine laboratory measurements, i.e., fasting insulin and triglycerides, provides a simple means of screening for insulin resistance in the general population.CONCLUSIONSA weighted combination of two routine laboratory measurements, i.e., fasting insulin and triglycerides, provides a simple means of screening for insulin resistance in the general population.
OBJECTIVE—Difficulties in measuring insulin sensitivity prevent the identification of insulin-resistant individuals in the general population. Therefore, we compared fasting insulin, homeostasis model assessment (HOMA), insulin-to-glucose ratio, Bennett index, and a score based on weighted combinations of fasting insulin, BMI, and fasting triglycerides with the euglycemic insulin clamp to determine the most appropriate method for assessing insulin resistance in the general population. RESEARCH DESIGN AND METHODS—Family history of diabetes, BMI, blood pressure, waist and hip circumference, fasting lipids, glucose, insulin, liver enzymes, and insulin sensitivity index (ISI) using the euglycemic insulin clamp were obtained for 178 normoglycemic individuals aged 25–68 years. Product-moment correlations were used to examine the association between ISI and various surrogate measurements of insulin sensitivity. Regression models were used to devise weights for each variable and to identify cutoff points for individual components of the score. A bootstrap procedure was used to identify the most useful predictors of ISI. RESULTS—Correlation coefficients between ISI and fasting insulin, HOMA, insulin-to-glucose ratio, and the Bennett index were similar in magnitude. The variables that best predicted insulin sensitivity were fasting insulin and fasting triglycerides. The use of a score based on \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \[Mffm{/}I{\,}{\,}{=}{\,}{\,}exp{[}2.63{\,}{\,}{-}{\,}{\,}0.28ln(insulin){\,}{\,}{-}{\,}{\,}0.31ln(TAG){]}\] \end{document}rather than the use of fasting insulin alone resulted in a higher sensitivity and a maintained specificity when predicting insulin sensitivity. CONCLUSIONS—A weighted combination of two routine laboratory measurements, i.e., fasting insulin and triglycerides, provides a simple means of screening for insulin resistance in the general population.
Audience Professional
Author Robert J. Walker
Lara A. Temple
Ashley W. Duncan
Kirsten A. McAuley
Nick J. Lewis-Barned
Sheila M. Williams
Jim I. Mann
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https://www.ncbi.nlm.nih.gov/pubmed/11289468$$D View this record in MEDLINE/PubMed
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Issue 3
Keywords Endocrinopathy
Human
Protein hormone
Body mass index
Target tissue resistance
Pancreatic hormone
Glucose clamp technique
Clinical biology
Diagnosis
Non insulin dependent diabetes
Insulin
Glycemia
Language English
License CC BY 4.0
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PublicationTitle Diabetes care
PublicationTitleAlternate Diabetes Care
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Snippet Diagnosing Insulin Resistance in the General Population Kirsten A. McAuley , MBCHB 1 , Sheila M. Williams , BSC(HONS) 2 , Jim I. Mann , DM, PHD, FRACP 1 ,...
OBJECTIVE—Difficulties in measuring insulin sensitivity prevent the identification of insulin-resistant individuals in the general population. Therefore, we...
Difficulties in measuring insulin sensitivity prevent the identification of insulin-resistant individuals in the general population. Therefore, we compared...
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SubjectTerms Adult
Aged
Biological and medical sciences
Blood Glucose - metabolism
Blood pressure
Body Constitution
Body Mass Index
Clinical medicine
Diabetes
Diabetes. Impaired glucose tolerance
Diagnosis
Endocrine pancreas. Apud cells (diseases)
Endocrinopathies
Enzymes
Female
Glucose
Glucose Clamp Technique
Homeostasis
Humans
Infusions, Intravenous
Insulin - administration & dosage
Insulin - blood
Insulin - pharmacology
Insulin Resistance
Male
Management. Various non-drug treatments. Langerhans islet grafts
Medical sciences
Middle Aged
Plasma
Research design
Risk Factors
Triglycerides
Triglycerides - blood
Variables
Title Diagnosing Insulin Resistance in the General Population
URI http://care.diabetesjournals.org/content/24/3/460.abstract
https://www.ncbi.nlm.nih.gov/pubmed/11289468
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Volume 24
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