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New measure of insulin sensitivity predicts cardiovascular disease better than HOMA estimated insulin resistance
Accurate assessment of insulin sensitivity may better identify individuals at increased risk of cardio-metabolic diseases. To examine whether a combination of anthropometric, biochemical and imaging measures can better estimate insulin sensitivity index (ISI) and provide improved prediction of cardi...
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| Published in: | PloS one 2013-09, Vol.8 (9), p.e74410-e74410 |
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| container_end_page | e74410 |
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| creator | Venkataraman, Kavita Khoo, Chin Meng Leow, Melvin K S Khoo, Eric Y H Isaac, Anburaj V Zagorodnov, Vitali Sadananthan, Suresh A Velan, Sendhil S Chong, Yap Seng Gluckman, Peter Lee, Jeannette Salim, Agus Tai, E Shyong Lee, Yung Seng |
| description | Accurate assessment of insulin sensitivity may better identify individuals at increased risk of cardio-metabolic diseases.
To examine whether a combination of anthropometric, biochemical and imaging measures can better estimate insulin sensitivity index (ISI) and provide improved prediction of cardio-metabolic risk, in comparison to HOMA-IR.
Healthy male volunteers (96 Chinese, 80 Malay, 77 Indian), 21 to 40 years, body mass index 18-30 kg/m(2). Predicted ISI (ISI-cal) was generated using 45 randomly selected Chinese through stepwise multiple linear regression, and validated in the rest using non-parametric correlation (Kendall's tau τ). In an independent longitudinal cohort, ISI-cal and HOMA-IR were compared for prediction of diabetes and cardiovascular disease (CVD), using ROC curves.
The study was conducted in a university academic medical centre.
ISI measured by hyperinsulinemic euglycemic glucose clamp, along with anthropometric measurements, biochemical assessment and imaging; incident diabetes and CVD.
A combination of fasting insulin, serum triglycerides and waist-to-hip ratio (WHR) provided the best estimate of clamp-derived ISI (adjusted R(2) 0.58 versus 0.32 HOMA-IR). In an independent cohort, ROC areas under the curve were 0.77±0.02 ISI-cal versus 0.76±0.02 HOMA-IR (p>0.05) for incident diabetes, and 0.74±0.03 ISI-cal versus 0.61±0.03 HOMA-IR (p |
| doi_str_mv | 10.1371/journal.pone.0074410 |
| format | article |
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To examine whether a combination of anthropometric, biochemical and imaging measures can better estimate insulin sensitivity index (ISI) and provide improved prediction of cardio-metabolic risk, in comparison to HOMA-IR.
Healthy male volunteers (96 Chinese, 80 Malay, 77 Indian), 21 to 40 years, body mass index 18-30 kg/m(2). Predicted ISI (ISI-cal) was generated using 45 randomly selected Chinese through stepwise multiple linear regression, and validated in the rest using non-parametric correlation (Kendall's tau τ). In an independent longitudinal cohort, ISI-cal and HOMA-IR were compared for prediction of diabetes and cardiovascular disease (CVD), using ROC curves.
The study was conducted in a university academic medical centre.
ISI measured by hyperinsulinemic euglycemic glucose clamp, along with anthropometric measurements, biochemical assessment and imaging; incident diabetes and CVD.
A combination of fasting insulin, serum triglycerides and waist-to-hip ratio (WHR) provided the best estimate of clamp-derived ISI (adjusted R(2) 0.58 versus 0.32 HOMA-IR). In an independent cohort, ROC areas under the curve were 0.77±0.02 ISI-cal versus 0.76±0.02 HOMA-IR (p>0.05) for incident diabetes, and 0.74±0.03 ISI-cal versus 0.61±0.03 HOMA-IR (p<0.001) for incident CVD. ISI-cal also had greater sensitivity than defined metabolic syndrome in predicting CVD, with a four-fold increase in the risk of CVD independent of metabolic syndrome.
Triglycerides and WHR, combined with fasting insulin levels, provide a better estimate of current insulin resistance state and improved identification of individuals with future risk of CVD, compared to HOMA-IR. This may be useful for estimating insulin sensitivity and cardio-metabolic risk in clinical and epidemiological settings.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0074410</identifier><identifier>PMID: 24098646</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Anthropometry ; Anthropometry - methods ; Body mass ; Body mass index ; Body measurements ; Body size ; Cardiovascular diseases ; Cardiovascular Diseases - epidemiology ; Cohort Studies ; Diabetes mellitus ; Epidemiology ; Fasting ; Glucose ; Glucose Clamp Technique ; Health care facilities ; Health risk assessment ; Health risks ; Hip ; Humans ; Insulin ; Insulin resistance ; Insulin Resistance - physiology ; Linear Models ; Longitudinal Studies ; Male ; Metabolic syndrome ; NMR ; Nuclear magnetic resonance ; Predictions ; Risk ; Risk Assessment - methods ; ROC Curve ; Science ; Sensitivity ; Sensitivity analysis ; Triglycerides</subject><ispartof>PloS one, 2013-09, Vol.8 (9), p.e74410-e74410</ispartof><rights>2013 Venkataraman et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2013 Venkataraman et al 2013 Venkataraman et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-cc36d432ebadd895d195674a2049ec3bb9aa24cff9df0b63ae6a8e9790a27f233</citedby><cites>FETCH-LOGICAL-c526t-cc36d432ebadd895d195674a2049ec3bb9aa24cff9df0b63ae6a8e9790a27f233</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1438035440/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1438035440?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24098646$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Andrews, Zane</contributor><creatorcontrib>Venkataraman, Kavita</creatorcontrib><creatorcontrib>Khoo, Chin Meng</creatorcontrib><creatorcontrib>Leow, Melvin K S</creatorcontrib><creatorcontrib>Khoo, Eric Y H</creatorcontrib><creatorcontrib>Isaac, Anburaj V</creatorcontrib><creatorcontrib>Zagorodnov, Vitali</creatorcontrib><creatorcontrib>Sadananthan, Suresh A</creatorcontrib><creatorcontrib>Velan, Sendhil S</creatorcontrib><creatorcontrib>Chong, Yap Seng</creatorcontrib><creatorcontrib>Gluckman, Peter</creatorcontrib><creatorcontrib>Lee, Jeannette</creatorcontrib><creatorcontrib>Salim, Agus</creatorcontrib><creatorcontrib>Tai, E Shyong</creatorcontrib><creatorcontrib>Lee, Yung Seng</creatorcontrib><title>New measure of insulin sensitivity predicts cardiovascular disease better than HOMA estimated insulin resistance</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Accurate assessment of insulin sensitivity may better identify individuals at increased risk of cardio-metabolic diseases.
To examine whether a combination of anthropometric, biochemical and imaging measures can better estimate insulin sensitivity index (ISI) and provide improved prediction of cardio-metabolic risk, in comparison to HOMA-IR.
Healthy male volunteers (96 Chinese, 80 Malay, 77 Indian), 21 to 40 years, body mass index 18-30 kg/m(2). Predicted ISI (ISI-cal) was generated using 45 randomly selected Chinese through stepwise multiple linear regression, and validated in the rest using non-parametric correlation (Kendall's tau τ). In an independent longitudinal cohort, ISI-cal and HOMA-IR were compared for prediction of diabetes and cardiovascular disease (CVD), using ROC curves.
The study was conducted in a university academic medical centre.
ISI measured by hyperinsulinemic euglycemic glucose clamp, along with anthropometric measurements, biochemical assessment and imaging; incident diabetes and CVD.
A combination of fasting insulin, serum triglycerides and waist-to-hip ratio (WHR) provided the best estimate of clamp-derived ISI (adjusted R(2) 0.58 versus 0.32 HOMA-IR). In an independent cohort, ROC areas under the curve were 0.77±0.02 ISI-cal versus 0.76±0.02 HOMA-IR (p>0.05) for incident diabetes, and 0.74±0.03 ISI-cal versus 0.61±0.03 HOMA-IR (p<0.001) for incident CVD. ISI-cal also had greater sensitivity than defined metabolic syndrome in predicting CVD, with a four-fold increase in the risk of CVD independent of metabolic syndrome.
Triglycerides and WHR, combined with fasting insulin levels, provide a better estimate of current insulin resistance state and improved identification of individuals with future risk of CVD, compared to HOMA-IR. This may be useful for estimating insulin sensitivity and cardio-metabolic risk in clinical and epidemiological settings.</description><subject>Adult</subject><subject>Anthropometry</subject><subject>Anthropometry - methods</subject><subject>Body mass</subject><subject>Body mass index</subject><subject>Body measurements</subject><subject>Body size</subject><subject>Cardiovascular diseases</subject><subject>Cardiovascular Diseases - epidemiology</subject><subject>Cohort Studies</subject><subject>Diabetes mellitus</subject><subject>Epidemiology</subject><subject>Fasting</subject><subject>Glucose</subject><subject>Glucose Clamp Technique</subject><subject>Health care facilities</subject><subject>Health risk assessment</subject><subject>Health risks</subject><subject>Hip</subject><subject>Humans</subject><subject>Insulin</subject><subject>Insulin resistance</subject><subject>Insulin Resistance - physiology</subject><subject>Linear Models</subject><subject>Longitudinal Studies</subject><subject>Male</subject><subject>Metabolic syndrome</subject><subject>NMR</subject><subject>Nuclear magnetic resonance</subject><subject>Predictions</subject><subject>Risk</subject><subject>Risk Assessment - methods</subject><subject>ROC Curve</subject><subject>Science</subject><subject>Sensitivity</subject><subject>Sensitivity analysis</subject><subject>Triglycerides</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptUk1v1DAUjBCIfsA_QGCJSy-72LHjjwtSVQGtVOgFztaL_dJ6lY0X29mq_54sm65axMmWPTNv5mmq6h2jS8YV-7SKYxqgX27igEtKlRCMvqiOmeH1QtaUv3xyP6pOcl5R2nAt5evqqBbUaCnkcbX5gfdkjZDHhCR2JAx57MNAMg45lLAN5YFsEvrgSiYOkg9xC9mNPSTiQ56ISFosBRMpdzCQy5vv5wRzCWso6A9yCXPIBQaHb6pXHfQZ387nafXr65efF5eL65tvVxfn1wvX1LIsnOPSC15jC95r03hmGqkE1FQYdLxtDUAtXNcZ39FWckAJGo0yFGrV1ZyfVh_2ups-ZjsvK1smuKa8EYJOiKs9wkdY2U2aLKcHGyHYvw8x3VpIJbgeLVOq5brxXCsQTGpDdWOkYaCEhq51k9bnedrYrtE7HEqC_pno858h3NnbuLVcaUVrPQmczQIp_h6nBdp1yA77HgaM48634NNkYXbJPv4D_X86sUe5FHNO2B3MMGp3BXpk2V2B7Fygifb-aZAD6bEx_A80XcZK</recordid><startdate>20130930</startdate><enddate>20130930</enddate><creator>Venkataraman, Kavita</creator><creator>Khoo, Chin Meng</creator><creator>Leow, Melvin K S</creator><creator>Khoo, Eric Y H</creator><creator>Isaac, Anburaj V</creator><creator>Zagorodnov, Vitali</creator><creator>Sadananthan, Suresh A</creator><creator>Velan, Sendhil S</creator><creator>Chong, Yap Seng</creator><creator>Gluckman, Peter</creator><creator>Lee, Jeannette</creator><creator>Salim, Agus</creator><creator>Tai, E Shyong</creator><creator>Lee, Yung Seng</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20130930</creationdate><title>New measure of insulin sensitivity predicts cardiovascular disease better than HOMA estimated insulin resistance</title><author>Venkataraman, Kavita ; Khoo, Chin Meng ; Leow, Melvin K S ; Khoo, Eric Y H ; Isaac, Anburaj V ; Zagorodnov, Vitali ; Sadananthan, Suresh A ; Velan, Sendhil S ; Chong, Yap Seng ; Gluckman, Peter ; Lee, Jeannette ; Salim, Agus ; Tai, E Shyong ; Lee, Yung Seng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c526t-cc36d432ebadd895d195674a2049ec3bb9aa24cff9df0b63ae6a8e9790a27f233</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Adult</topic><topic>Anthropometry</topic><topic>Anthropometry - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Venkataraman, Kavita</au><au>Khoo, Chin Meng</au><au>Leow, Melvin K S</au><au>Khoo, Eric Y H</au><au>Isaac, Anburaj V</au><au>Zagorodnov, Vitali</au><au>Sadananthan, Suresh A</au><au>Velan, Sendhil S</au><au>Chong, Yap Seng</au><au>Gluckman, Peter</au><au>Lee, Jeannette</au><au>Salim, Agus</au><au>Tai, E Shyong</au><au>Lee, Yung Seng</au><au>Andrews, Zane</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>New measure of insulin sensitivity predicts cardiovascular disease better than HOMA estimated insulin resistance</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2013-09-30</date><risdate>2013</risdate><volume>8</volume><issue>9</issue><spage>e74410</spage><epage>e74410</epage><pages>e74410-e74410</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Accurate assessment of insulin sensitivity may better identify individuals at increased risk of cardio-metabolic diseases.
To examine whether a combination of anthropometric, biochemical and imaging measures can better estimate insulin sensitivity index (ISI) and provide improved prediction of cardio-metabolic risk, in comparison to HOMA-IR.
Healthy male volunteers (96 Chinese, 80 Malay, 77 Indian), 21 to 40 years, body mass index 18-30 kg/m(2). Predicted ISI (ISI-cal) was generated using 45 randomly selected Chinese through stepwise multiple linear regression, and validated in the rest using non-parametric correlation (Kendall's tau τ). In an independent longitudinal cohort, ISI-cal and HOMA-IR were compared for prediction of diabetes and cardiovascular disease (CVD), using ROC curves.
The study was conducted in a university academic medical centre.
ISI measured by hyperinsulinemic euglycemic glucose clamp, along with anthropometric measurements, biochemical assessment and imaging; incident diabetes and CVD.
A combination of fasting insulin, serum triglycerides and waist-to-hip ratio (WHR) provided the best estimate of clamp-derived ISI (adjusted R(2) 0.58 versus 0.32 HOMA-IR). In an independent cohort, ROC areas under the curve were 0.77±0.02 ISI-cal versus 0.76±0.02 HOMA-IR (p>0.05) for incident diabetes, and 0.74±0.03 ISI-cal versus 0.61±0.03 HOMA-IR (p<0.001) for incident CVD. ISI-cal also had greater sensitivity than defined metabolic syndrome in predicting CVD, with a four-fold increase in the risk of CVD independent of metabolic syndrome.
Triglycerides and WHR, combined with fasting insulin levels, provide a better estimate of current insulin resistance state and improved identification of individuals with future risk of CVD, compared to HOMA-IR. This may be useful for estimating insulin sensitivity and cardio-metabolic risk in clinical and epidemiological settings.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>24098646</pmid><doi>10.1371/journal.pone.0074410</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2013-09, Vol.8 (9), p.e74410-e74410 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1438035440 |
| source | Open Access: PubMed Central; ProQuest - Publicly Available Content Database |
| subjects | Adult Anthropometry Anthropometry - methods Body mass Body mass index Body measurements Body size Cardiovascular diseases Cardiovascular Diseases - epidemiology Cohort Studies Diabetes mellitus Epidemiology Fasting Glucose Glucose Clamp Technique Health care facilities Health risk assessment Health risks Hip Humans Insulin Insulin resistance Insulin Resistance - physiology Linear Models Longitudinal Studies Male Metabolic syndrome NMR Nuclear magnetic resonance Predictions Risk Risk Assessment - methods ROC Curve Science Sensitivity Sensitivity analysis Triglycerides |
| title | New measure of insulin sensitivity predicts cardiovascular disease better than HOMA estimated insulin resistance |
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