Development and validation of a prediction model estimating the 10-year risk for type 2 diabetes in China
To derive and validate a concise prediction model estimating the 10-year risk for type 2 diabetes (T2DM) in China. A total of 11494 subjects from the China Health and Nutrition Survey recorded from 2004 to 2015 were analyzed and only 6023 participants were enrolled in this study. Four logistic model...
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| Veröffentlicht in: | PloS one 2020-09, Vol.15 (9), p.e0237936 |
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| creator | Shao, Xian Wang, Yao Huang, Shuai Liu, Hongyan Zhou, Saijun Zhang, Rui Yu, Pei |
| description | To derive and validate a concise prediction model estimating the 10-year risk for type 2 diabetes (T2DM) in China.
A total of 11494 subjects from the China Health and Nutrition Survey recorded from 2004 to 2015 were analyzed and only 6023 participants were enrolled in this study. Four logistic models were analyzed using the derivation cohort. Methods of calibration and discrimination were used for the validation cohort.
In the derivation cohort, 257 patients were identified from a total of 4498 cases. In the validation cohort, 92 patients were identified from a total of 1525 cases. Four models performed nicely for both calibration and discrimination. The AUC in the derivation cohort for models A, B, C and D were 0.788 (0.761-0.816), 0.807 (0.780-0.834), 0.905 (0.879-0.932) and 0.882 (0.853-0.912), respectively. The Youden index for models A, B, C and D were 1.46, 1.48, 1.67 and 1.65, respectively. Model C showed the highest sensitivity and model D showed the highest specificity.
Models A and B were non-invasive and can be used to identify high-risk patients for broad screening. Models C and D may be used to provide more accurate assessments of diabetes risk. Furthermore, model C showed the best performance for predicting T2DM risk and identifying individuals who are in need of interventions, current approach improvement and additional follow-up. |
| doi_str_mv | 10.1371/journal.pone.0237936 |
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A total of 11494 subjects from the China Health and Nutrition Survey recorded from 2004 to 2015 were analyzed and only 6023 participants were enrolled in this study. Four logistic models were analyzed using the derivation cohort. Methods of calibration and discrimination were used for the validation cohort.
In the derivation cohort, 257 patients were identified from a total of 4498 cases. In the validation cohort, 92 patients were identified from a total of 1525 cases. Four models performed nicely for both calibration and discrimination. The AUC in the derivation cohort for models A, B, C and D were 0.788 (0.761-0.816), 0.807 (0.780-0.834), 0.905 (0.879-0.932) and 0.882 (0.853-0.912), respectively. The Youden index for models A, B, C and D were 1.46, 1.48, 1.67 and 1.65, respectively. Model C showed the highest sensitivity and model D showed the highest specificity.
Models A and B were non-invasive and can be used to identify high-risk patients for broad screening. Models C and D may be used to provide more accurate assessments of diabetes risk. Furthermore, model C showed the best performance for predicting T2DM risk and identifying individuals who are in need of interventions, current approach improvement and additional follow-up.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0237936</identifier><identifier>PMID: 32881911</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Age ; Area Under Curve ; Biology and Life Sciences ; Blood Glucose - analysis ; Blood pressure ; Body mass index ; Calibration ; China ; Derivation ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus, Type 2 - diagnosis ; Diabetes Mellitus, Type 2 - etiology ; Endocrinology ; Estimation ; Exercise ; Female ; Glucose ; Glycated Hemoglobin - analysis ; Health risks ; Hemoglobin ; Hormones ; Hospitals ; Humans ; Laboratories ; Logistic Models ; Male ; Medicine and Health Sciences ; Metabolic disorders ; Methods ; Middle Aged ; Middle schools ; Models, Theoretical ; Nutrition ; Nutrition Surveys ; Performance prediction ; Physical Sciences ; Population ; Prediction models ; Public health ; Research and Analysis Methods ; Risk Factors ; Risk groups ; ROC Curve ; Sample size ; Sleep ; Sociodemographics</subject><ispartof>PloS one, 2020-09, Vol.15 (9), p.e0237936</ispartof><rights>2020 Shao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://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>2020 Shao et al 2020 Shao et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-afe8e9f6cc798ff718bfaab4a3875c2915b2f3f41f7bab23f04cfac8b7658b9b3</citedby><cites>FETCH-LOGICAL-c526t-afe8e9f6cc798ff718bfaab4a3875c2915b2f3f41f7bab23f04cfac8b7658b9b3</cites><orcidid>0000-0001-7992-0480</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7470416/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7470416/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32881911$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Hu, Cheng</contributor><creatorcontrib>Shao, Xian</creatorcontrib><creatorcontrib>Wang, Yao</creatorcontrib><creatorcontrib>Huang, Shuai</creatorcontrib><creatorcontrib>Liu, Hongyan</creatorcontrib><creatorcontrib>Zhou, Saijun</creatorcontrib><creatorcontrib>Zhang, Rui</creatorcontrib><creatorcontrib>Yu, Pei</creatorcontrib><title>Development and validation of a prediction model estimating the 10-year risk for type 2 diabetes in China</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>To derive and validate a concise prediction model estimating the 10-year risk for type 2 diabetes (T2DM) in China.
A total of 11494 subjects from the China Health and Nutrition Survey recorded from 2004 to 2015 were analyzed and only 6023 participants were enrolled in this study. Four logistic models were analyzed using the derivation cohort. Methods of calibration and discrimination were used for the validation cohort.
In the derivation cohort, 257 patients were identified from a total of 4498 cases. In the validation cohort, 92 patients were identified from a total of 1525 cases. Four models performed nicely for both calibration and discrimination. The AUC in the derivation cohort for models A, B, C and D were 0.788 (0.761-0.816), 0.807 (0.780-0.834), 0.905 (0.879-0.932) and 0.882 (0.853-0.912), respectively. The Youden index for models A, B, C and D were 1.46, 1.48, 1.67 and 1.65, respectively. Model C showed the highest sensitivity and model D showed the highest specificity.
Models A and B were non-invasive and can be used to identify high-risk patients for broad screening. Models C and D may be used to provide more accurate assessments of diabetes risk. Furthermore, model C showed the best performance for predicting T2DM risk and identifying individuals who are in need of interventions, current approach improvement and additional follow-up.</description><subject>Age</subject><subject>Area Under Curve</subject><subject>Biology and Life Sciences</subject><subject>Blood Glucose - analysis</subject><subject>Blood pressure</subject><subject>Body mass index</subject><subject>Calibration</subject><subject>China</subject><subject>Derivation</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetes Mellitus, Type 2 - diagnosis</subject><subject>Diabetes Mellitus, Type 2 - etiology</subject><subject>Endocrinology</subject><subject>Estimation</subject><subject>Exercise</subject><subject>Female</subject><subject>Glucose</subject><subject>Glycated Hemoglobin - analysis</subject><subject>Health 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Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shao, Xian</au><au>Wang, Yao</au><au>Huang, Shuai</au><au>Liu, Hongyan</au><au>Zhou, Saijun</au><au>Zhang, Rui</au><au>Yu, Pei</au><au>Hu, Cheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development and validation of a prediction model estimating the 10-year risk for type 2 diabetes in China</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-09-03</date><risdate>2020</risdate><volume>15</volume><issue>9</issue><spage>e0237936</spage><pages>e0237936-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>To derive and validate a concise prediction model estimating the 10-year risk for type 2 diabetes (T2DM) in China.
A total of 11494 subjects from the China Health and Nutrition Survey recorded from 2004 to 2015 were analyzed and only 6023 participants were enrolled in this study. Four logistic models were analyzed using the derivation cohort. Methods of calibration and discrimination were used for the validation cohort.
In the derivation cohort, 257 patients were identified from a total of 4498 cases. In the validation cohort, 92 patients were identified from a total of 1525 cases. Four models performed nicely for both calibration and discrimination. The AUC in the derivation cohort for models A, B, C and D were 0.788 (0.761-0.816), 0.807 (0.780-0.834), 0.905 (0.879-0.932) and 0.882 (0.853-0.912), respectively. The Youden index for models A, B, C and D were 1.46, 1.48, 1.67 and 1.65, respectively. Model C showed the highest sensitivity and model D showed the highest specificity.
Models A and B were non-invasive and can be used to identify high-risk patients for broad screening. Models C and D may be used to provide more accurate assessments of diabetes risk. Furthermore, model C showed the best performance for predicting T2DM risk and identifying individuals who are in need of interventions, current approach improvement and additional follow-up.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32881911</pmid><doi>10.1371/journal.pone.0237936</doi><orcidid>https://orcid.org/0000-0001-7992-0480</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Age Area Under Curve Biology and Life Sciences Blood Glucose - analysis Blood pressure Body mass index Calibration China Derivation Diabetes Diabetes mellitus Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - diagnosis Diabetes Mellitus, Type 2 - etiology Endocrinology Estimation Exercise Female Glucose Glycated Hemoglobin - analysis Health risks Hemoglobin Hormones Hospitals Humans Laboratories Logistic Models Male Medicine and Health Sciences Metabolic disorders Methods Middle Aged Middle schools Models, Theoretical Nutrition Nutrition Surveys Performance prediction Physical Sciences Population Prediction models Public health Research and Analysis Methods Risk Factors Risk groups ROC Curve Sample size Sleep Sociodemographics |
| title | Development and validation of a prediction model estimating the 10-year risk for type 2 diabetes in China |
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