Development and validation of a predictive model of acute glucose response to exercise in individuals with type 2 diabetes
Our purpose was to develop and test a predictive model of the acute glucose response to exercise in individuals with type 2 diabetes. Data from three previous exercise studies (56 subjects, 488 exercise sessions) were combined and used as a development dataset. A mixed-effects Least Absolute Shrinka...
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| Veröffentlicht in: | Diabetology and metabolic syndrome 2013-07, Vol.5 (1), p.33-33, Article 33 |
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| creator | Gibson, Bryan S Colberg, Sheri R Poirier, Paul Vancea, Denise Maria Martins Jones, Jason Marcus, Robin |
| description | Our purpose was to develop and test a predictive model of the acute glucose response to exercise in individuals with type 2 diabetes.
Data from three previous exercise studies (56 subjects, 488 exercise sessions) were combined and used as a development dataset. A mixed-effects Least Absolute Shrinkage Selection Operator (LASSO) was used to select predictors among 12 potential predictors. Tests of the relative importance of each predictor were conducted using the Lindemann Merenda and Gold (LMG) algorithm. Model structure was tested using likelihood ratio tests. Model accuracy in the development dataset was assessed by leave-one-out cross-validation.Prospectively captured data (47 individuals, 436 sessions) was used as a test dataset. Model accuracy was calculated as the percentage of predictions within measurement error. Overall model utility was assessed as the number of subjects with ≤1 model error after the third exercise session. Model accuracy across individuals was assessed graphically. In a post-hoc analysis, a mixed-effects logistic regression tested the association of individuals' attributes with model error.
Minutes since eating, a non-linear transformation of minutes since eating, post-prandial state, hemoglobin A1c, sulfonylurea status, age, and exercise session number were identified as novel predictors. Minutes since eating, its transformations, and hemoglobin A1c combined to account for 19.6% of the variance in glucose response. Sulfonylurea status, age, and exercise session each accounted for |
| doi_str_mv | 10.1186/1758-5996-5-33 |
| format | Article |
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Data from three previous exercise studies (56 subjects, 488 exercise sessions) were combined and used as a development dataset. A mixed-effects Least Absolute Shrinkage Selection Operator (LASSO) was used to select predictors among 12 potential predictors. Tests of the relative importance of each predictor were conducted using the Lindemann Merenda and Gold (LMG) algorithm. Model structure was tested using likelihood ratio tests. Model accuracy in the development dataset was assessed by leave-one-out cross-validation.Prospectively captured data (47 individuals, 436 sessions) was used as a test dataset. Model accuracy was calculated as the percentage of predictions within measurement error. Overall model utility was assessed as the number of subjects with ≤1 model error after the third exercise session. Model accuracy across individuals was assessed graphically. In a post-hoc analysis, a mixed-effects logistic regression tested the association of individuals' attributes with model error.
Minutes since eating, a non-linear transformation of minutes since eating, post-prandial state, hemoglobin A1c, sulfonylurea status, age, and exercise session number were identified as novel predictors. Minutes since eating, its transformations, and hemoglobin A1c combined to account for 19.6% of the variance in glucose response. Sulfonylurea status, age, and exercise session each accounted for <1.0% of the variance. In the development dataset, a model with random slopes for pre-exercise glucose improved fit over a model with random intercepts only (likelihood ratio 34.5, p < 0.001). Cross-validated model accuracy was 83.3%.In the test dataset, overall accuracy was 80.2%. The model was more accurate in pre-prandial than postprandial exercise (83.6% vs. 74.5% accuracy respectively). 31/47 subjects had ≤1 model error after the third exercise session. Model error varied across individuals and was weakly associated with within-subject variability in pre-exercise glucose (Odds ratio 1.49, 95% Confidence interval 1.23-1.75).
The preliminary development and test of a predictive model of acute glucose response to exercise is presented. Further work to improve this model is discussed.</description><identifier>ISSN: 1758-5996</identifier><identifier>EISSN: 1758-5996</identifier><identifier>DOI: 10.1186/1758-5996-5-33</identifier><identifier>PMID: 23816355</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Blood sugar ; Care and treatment ; Diabetes ; Exercise ; Glucose ; Glycosylated hemoglobin ; Health aspects ; Physiological aspects ; Studies ; Type 2 diabetes</subject><ispartof>Diabetology and metabolic syndrome, 2013-07, Vol.5 (1), p.33-33, Article 33</ispartof><rights>COPYRIGHT 2013 BioMed Central Ltd.</rights><rights>2013 Gibson et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><rights>Copyright © 2013 Gibson et al.; licensee BioMed Central Ltd. 2013 Gibson et al.; licensee BioMed Central Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b607t-5c512bfe19173042870693b0d1e2aaebb04e402c9ee5a78b69ac91e512d2820c3</citedby><cites>FETCH-LOGICAL-b607t-5c512bfe19173042870693b0d1e2aaebb04e402c9ee5a78b69ac91e512d2820c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701573/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701573/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23816355$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gibson, Bryan S</creatorcontrib><creatorcontrib>Colberg, Sheri R</creatorcontrib><creatorcontrib>Poirier, Paul</creatorcontrib><creatorcontrib>Vancea, Denise Maria Martins</creatorcontrib><creatorcontrib>Jones, Jason</creatorcontrib><creatorcontrib>Marcus, Robin</creatorcontrib><title>Development and validation of a predictive model of acute glucose response to exercise in individuals with type 2 diabetes</title><title>Diabetology and metabolic syndrome</title><addtitle>Diabetol Metab Syndr</addtitle><description>Our purpose was to develop and test a predictive model of the acute glucose response to exercise in individuals with type 2 diabetes.
Data from three previous exercise studies (56 subjects, 488 exercise sessions) were combined and used as a development dataset. A mixed-effects Least Absolute Shrinkage Selection Operator (LASSO) was used to select predictors among 12 potential predictors. Tests of the relative importance of each predictor were conducted using the Lindemann Merenda and Gold (LMG) algorithm. Model structure was tested using likelihood ratio tests. Model accuracy in the development dataset was assessed by leave-one-out cross-validation.Prospectively captured data (47 individuals, 436 sessions) was used as a test dataset. Model accuracy was calculated as the percentage of predictions within measurement error. Overall model utility was assessed as the number of subjects with ≤1 model error after the third exercise session. Model accuracy across individuals was assessed graphically. In a post-hoc analysis, a mixed-effects logistic regression tested the association of individuals' attributes with model error.
Minutes since eating, a non-linear transformation of minutes since eating, post-prandial state, hemoglobin A1c, sulfonylurea status, age, and exercise session number were identified as novel predictors. Minutes since eating, its transformations, and hemoglobin A1c combined to account for 19.6% of the variance in glucose response. Sulfonylurea status, age, and exercise session each accounted for <1.0% of the variance. In the development dataset, a model with random slopes for pre-exercise glucose improved fit over a model with random intercepts only (likelihood ratio 34.5, p < 0.001). Cross-validated model accuracy was 83.3%.In the test dataset, overall accuracy was 80.2%. The model was more accurate in pre-prandial than postprandial exercise (83.6% vs. 74.5% accuracy respectively). 31/47 subjects had ≤1 model error after the third exercise session. Model error varied across individuals and was weakly associated with within-subject variability in pre-exercise glucose (Odds ratio 1.49, 95% Confidence interval 1.23-1.75).
The preliminary development and test of a predictive model of acute glucose response to exercise is presented. Further work to improve this model is discussed.</description><subject>Blood sugar</subject><subject>Care and treatment</subject><subject>Diabetes</subject><subject>Exercise</subject><subject>Glucose</subject><subject>Glycosylated hemoglobin</subject><subject>Health aspects</subject><subject>Physiological aspects</subject><subject>Studies</subject><subject>Type 2 diabetes</subject><issn>1758-5996</issn><issn>1758-5996</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNUl2L1DAULaK46-irjxIQxJeuSdM0zYswrJ-w4Is-hzS5ncmSJrVJR9dfb7q7jjO6giQkl3vPOdyc3KJ4SvAZIW3zinDWlkyIpmQlpfeK033i_kF8UjyK8RLjhjNePyxOKtqShjJ2Wvx4AztwYRzAJ6S8QTvlrFHJBo9CjxQaJzBWJ7sDNAQD7jqr5wRo42YdIqAJ4hh8DlJA8B0mbXNsfd7G7qyZlYvom01blK5GQBUyVnWQID4uHvS5Bk9u71Xx5d3bz-cfyotP7z-ery_KrsE8lUwzUnU9EEE4xXXVctwI2mFDoFIKug7XUONKCwCmeNs1QmlBIJNM1VZY01Xx-kZ3nLsBjM4vnZST42QHNV3JoKw8rni7lZuwk5RjwjjNAusbgc6GfwgcV3QY5OK9XLyXTNJF4-VtE1P4OkNMcrBRg3PKQ5ijJK3gVGDyP1Aq2poymo9V8fwP6GWYJ5_dvEa1nHIhfqM2yoG0vg-5S72IyjWjNW95RZuMOrsDlZeBwergobc5f0R4cUDYgnJpG4Obl9mJdyrrKcQ4Qb-3jmC5TPHfZj07_LE9_NfY0p9B6uzE</recordid><startdate>20130701</startdate><enddate>20130701</enddate><creator>Gibson, Bryan S</creator><creator>Colberg, Sheri R</creator><creator>Poirier, Paul</creator><creator>Vancea, Denise Maria Martins</creator><creator>Jones, Jason</creator><creator>Marcus, Robin</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7TS</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>5PM</scope></search><sort><creationdate>20130701</creationdate><title>Development and validation of a predictive model of acute glucose response to exercise in individuals with type 2 diabetes</title><author>Gibson, Bryan S ; Colberg, Sheri R ; Poirier, Paul ; Vancea, Denise Maria Martins ; Jones, Jason ; Marcus, Robin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b607t-5c512bfe19173042870693b0d1e2aaebb04e402c9ee5a78b69ac91e512d2820c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Blood sugar</topic><topic>Care and treatment</topic><topic>Diabetes</topic><topic>Exercise</topic><topic>Glucose</topic><topic>Glycosylated hemoglobin</topic><topic>Health aspects</topic><topic>Physiological aspects</topic><topic>Studies</topic><topic>Type 2 diabetes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gibson, Bryan S</creatorcontrib><creatorcontrib>Colberg, Sheri R</creatorcontrib><creatorcontrib>Poirier, Paul</creatorcontrib><creatorcontrib>Vancea, Denise Maria Martins</creatorcontrib><creatorcontrib>Jones, Jason</creatorcontrib><creatorcontrib>Marcus, Robin</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Physical Education Index</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Diabetology and metabolic syndrome</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gibson, Bryan S</au><au>Colberg, Sheri R</au><au>Poirier, Paul</au><au>Vancea, Denise Maria Martins</au><au>Jones, Jason</au><au>Marcus, Robin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development and validation of a predictive model of acute glucose response to exercise in individuals with type 2 diabetes</atitle><jtitle>Diabetology and metabolic syndrome</jtitle><addtitle>Diabetol Metab Syndr</addtitle><date>2013-07-01</date><risdate>2013</risdate><volume>5</volume><issue>1</issue><spage>33</spage><epage>33</epage><pages>33-33</pages><artnum>33</artnum><issn>1758-5996</issn><eissn>1758-5996</eissn><abstract>Our purpose was to develop and test a predictive model of the acute glucose response to exercise in individuals with type 2 diabetes.
Data from three previous exercise studies (56 subjects, 488 exercise sessions) were combined and used as a development dataset. A mixed-effects Least Absolute Shrinkage Selection Operator (LASSO) was used to select predictors among 12 potential predictors. Tests of the relative importance of each predictor were conducted using the Lindemann Merenda and Gold (LMG) algorithm. Model structure was tested using likelihood ratio tests. Model accuracy in the development dataset was assessed by leave-one-out cross-validation.Prospectively captured data (47 individuals, 436 sessions) was used as a test dataset. Model accuracy was calculated as the percentage of predictions within measurement error. Overall model utility was assessed as the number of subjects with ≤1 model error after the third exercise session. Model accuracy across individuals was assessed graphically. In a post-hoc analysis, a mixed-effects logistic regression tested the association of individuals' attributes with model error.
Minutes since eating, a non-linear transformation of minutes since eating, post-prandial state, hemoglobin A1c, sulfonylurea status, age, and exercise session number were identified as novel predictors. Minutes since eating, its transformations, and hemoglobin A1c combined to account for 19.6% of the variance in glucose response. Sulfonylurea status, age, and exercise session each accounted for <1.0% of the variance. In the development dataset, a model with random slopes for pre-exercise glucose improved fit over a model with random intercepts only (likelihood ratio 34.5, p < 0.001). Cross-validated model accuracy was 83.3%.In the test dataset, overall accuracy was 80.2%. The model was more accurate in pre-prandial than postprandial exercise (83.6% vs. 74.5% accuracy respectively). 31/47 subjects had ≤1 model error after the third exercise session. Model error varied across individuals and was weakly associated with within-subject variability in pre-exercise glucose (Odds ratio 1.49, 95% Confidence interval 1.23-1.75).
The preliminary development and test of a predictive model of acute glucose response to exercise is presented. Further work to improve this model is discussed.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>23816355</pmid><doi>10.1186/1758-5996-5-33</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Blood sugar Care and treatment Diabetes Exercise Glucose Glycosylated hemoglobin Health aspects Physiological aspects Studies Type 2 diabetes |
| title | Development and validation of a predictive model of acute glucose response to exercise in individuals with type 2 diabetes |
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