Identifying type 1 and 2 diabetes in research datasets where classification biomarkers are unavailable: assessing the accuracy of published approaches
We aimed to compare the performance of approaches for classifying insulin-treated diabetes within research datasets without measured classification biomarkers, evaluated against two independent biological definitions of diabetes type. We compared accuracy of ten reported approaches for classifying i...
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| Veröffentlicht in: | Journal of clinical epidemiology 2023-01, Vol.153, p.34-44 |
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| creator | Thomas, Nicholas J. McGovern, Andrew Young, Katherine G. Sharp, Seth A. Weedon, Michael N. Hattersley, Andrew T. Dennis, John Jones, Angus G. |
| description | We aimed to compare the performance of approaches for classifying insulin-treated diabetes within research datasets without measured classification biomarkers, evaluated against two independent biological definitions of diabetes type.
We compared accuracy of ten reported approaches for classifying insulin-treated diabetes into type 1 (T1D) and type 2 (T2D) diabetes in two cohorts: UK Biobank (UKBB) n = 26,399 and Diabetes Alliance for Research in England (DARE) n = 1,296. The overall performance for classifying T1D and T2D was assessed using: a T1D genetic risk score and genetic stratification method (UKBB); C-peptide measured at >3 years diabetes duration (DARE).
Approaches’ accuracy ranged from 71% to 88% (UKBB) and 68% to 88% (DARE). When classifying all participants, combining early insulin requirement with a T1D probability model (incorporating diagnosis age and body image issue [BMI]), and interview-reported diabetes type (UKBB available in only 15%) consistently achieved high accuracy (UKBB 87% and 87% and DARE 85% and 88%, respectively). For identifying T1D with minimal misclassification, models with high thresholds or young diagnosis age ( |
| doi_str_mv | 10.1016/j.jclinepi.2022.10.022 |
| format | Article |
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We compared accuracy of ten reported approaches for classifying insulin-treated diabetes into type 1 (T1D) and type 2 (T2D) diabetes in two cohorts: UK Biobank (UKBB) n = 26,399 and Diabetes Alliance for Research in England (DARE) n = 1,296. The overall performance for classifying T1D and T2D was assessed using: a T1D genetic risk score and genetic stratification method (UKBB); C-peptide measured at >3 years diabetes duration (DARE).
Approaches’ accuracy ranged from 71% to 88% (UKBB) and 68% to 88% (DARE). When classifying all participants, combining early insulin requirement with a T1D probability model (incorporating diagnosis age and body image issue [BMI]), and interview-reported diabetes type (UKBB available in only 15%) consistently achieved high accuracy (UKBB 87% and 87% and DARE 85% and 88%, respectively). For identifying T1D with minimal misclassification, models with high thresholds or young diagnosis age (<20 years) had highest performance. Findings were incorporated into an online tool identifying optimum approaches based on variable availability.
Models combining continuous features with early insulin requirement are the most accurate methods for classifying insulin-treated diabetes in research datasets without measured classification biomarkers.</description><identifier>ISSN: 0895-4356</identifier><identifier>EISSN: 1878-5921</identifier><identifier>DOI: 10.1016/j.jclinepi.2022.10.022</identifier><identifier>PMID: 36368478</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Accuracy ; Adult ; Age ; Availability ; Biobanks ; Biomarkers ; Body image ; Body mass index ; Classification ; Cohort stratification ; Datasets ; Diabetes ; Diabetes classification ; Diabetes epidemiology ; Diabetes mellitus (insulin dependent) ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus, Type 1 - drug therapy ; Diabetes Mellitus, Type 1 - genetics ; Diabetes Mellitus, Type 2 - diagnosis ; Diabetes Mellitus, Type 2 - drug therapy ; Diabetes Mellitus, Type 2 - genetics ; Diagnosis ; Ethnicity ; Humans ; Insulin ; Insulin - therapeutic use ; Peptides ; Population studies ; Primary care ; Risk Factors ; Statistical analysis ; Type 1 diabetes ; Type 2 diabetes ; Young Adult</subject><ispartof>Journal of clinical epidemiology, 2023-01, Vol.153, p.34-44</ispartof><rights>2022 The Author(s)</rights><rights>Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.</rights><rights>2022. The Author(s)</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c444t-781129122e8b47d98b9439be30968bc667a9faa213260f0546e6d9cea684e963</citedby><cites>FETCH-LOGICAL-c444t-781129122e8b47d98b9439be30968bc667a9faa213260f0546e6d9cea684e963</cites><orcidid>0000-0003-2570-3864 ; 0000-0002-7171-732X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2768682924?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,778,782,3539,27911,27912,45982,64370,64372,64374,72224</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36368478$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Thomas, Nicholas J.</creatorcontrib><creatorcontrib>McGovern, Andrew</creatorcontrib><creatorcontrib>Young, Katherine G.</creatorcontrib><creatorcontrib>Sharp, Seth A.</creatorcontrib><creatorcontrib>Weedon, Michael N.</creatorcontrib><creatorcontrib>Hattersley, Andrew T.</creatorcontrib><creatorcontrib>Dennis, John</creatorcontrib><creatorcontrib>Jones, Angus G.</creatorcontrib><title>Identifying type 1 and 2 diabetes in research datasets where classification biomarkers are unavailable: assessing the accuracy of published approaches</title><title>Journal of clinical epidemiology</title><addtitle>J Clin Epidemiol</addtitle><description>We aimed to compare the performance of approaches for classifying insulin-treated diabetes within research datasets without measured classification biomarkers, evaluated against two independent biological definitions of diabetes type.
We compared accuracy of ten reported approaches for classifying insulin-treated diabetes into type 1 (T1D) and type 2 (T2D) diabetes in two cohorts: UK Biobank (UKBB) n = 26,399 and Diabetes Alliance for Research in England (DARE) n = 1,296. The overall performance for classifying T1D and T2D was assessed using: a T1D genetic risk score and genetic stratification method (UKBB); C-peptide measured at >3 years diabetes duration (DARE).
Approaches’ accuracy ranged from 71% to 88% (UKBB) and 68% to 88% (DARE). When classifying all participants, combining early insulin requirement with a T1D probability model (incorporating diagnosis age and body image issue [BMI]), and interview-reported diabetes type (UKBB available in only 15%) consistently achieved high accuracy (UKBB 87% and 87% and DARE 85% and 88%, respectively). For identifying T1D with minimal misclassification, models with high thresholds or young diagnosis age (<20 years) had highest performance. Findings were incorporated into an online tool identifying optimum approaches based on variable availability.
Models combining continuous features with early insulin requirement are the most accurate methods for classifying insulin-treated diabetes in research datasets without measured classification biomarkers.</description><subject>Accuracy</subject><subject>Adult</subject><subject>Age</subject><subject>Availability</subject><subject>Biobanks</subject><subject>Biomarkers</subject><subject>Body image</subject><subject>Body mass index</subject><subject>Classification</subject><subject>Cohort stratification</subject><subject>Datasets</subject><subject>Diabetes</subject><subject>Diabetes classification</subject><subject>Diabetes epidemiology</subject><subject>Diabetes mellitus (insulin dependent)</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetes Mellitus, Type 1 - drug therapy</subject><subject>Diabetes Mellitus, Type 1 - genetics</subject><subject>Diabetes Mellitus, Type 2 - diagnosis</subject><subject>Diabetes Mellitus, Type 2 - drug therapy</subject><subject>Diabetes Mellitus, Type 2 - genetics</subject><subject>Diagnosis</subject><subject>Ethnicity</subject><subject>Humans</subject><subject>Insulin</subject><subject>Insulin - therapeutic use</subject><subject>Peptides</subject><subject>Population studies</subject><subject>Primary care</subject><subject>Risk Factors</subject><subject>Statistical analysis</subject><subject>Type 1 diabetes</subject><subject>Type 2 diabetes</subject><subject>Young Adult</subject><issn>0895-4356</issn><issn>1878-5921</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkc1u1TAQhS0EopfCK1SW2LDJxXYS_7ACVfxUqsSme2tiT4hDrhPspNV9EZ4XX92WBRtWRxp_M2c8h5Arzvaccfl-3I9uChGXsBdMiFLcF3lGdlwrXbVG8Odkx7Rpq6Zu5QV5lfPIGFdMtS_JRS1rqRuld-T3jce4hv4Y4g-6HheknEL0VFAfoMMVMw2RJswIyQ3UwwoZ10wfBkxI3QQ5hz44WMMcaRfmA6SfmDKF8rpFuIcwQTfhB1pALOzJZUAKzm0J3JHOPV22bgp5QE9hWdIMbsD8mrzoYcr45lEvyd2Xz3fX36rb719vrj_dVq5pmrVSmnNhuBCou0Z5ozvT1KbDmhmpOyelAtMDCF4LyXrWNhKlNw6h_B6NrC_Ju_PY4vtrw7zaQ8gOpwkizlu2QtWtVqptVUHf_oOO85ZiWa5QUkstjGgKJc-US3POCXu7pFBucrSc2VNwdrRPwdlTcKd6kdJ49Th-6w7o_7Y9JVWAj2cAyznuAyabXcDo0IeEbrV-Dv_z-AMyR67Z</recordid><startdate>202301</startdate><enddate>202301</enddate><creator>Thomas, Nicholas J.</creator><creator>McGovern, Andrew</creator><creator>Young, Katherine G.</creator><creator>Sharp, Seth A.</creator><creator>Weedon, Michael N.</creator><creator>Hattersley, Andrew T.</creator><creator>Dennis, John</creator><creator>Jones, Angus G.</creator><general>Elsevier Inc</general><general>Elsevier Limited</general><scope>6I.</scope><scope>AAFTH</scope><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>7QL</scope><scope>7QP</scope><scope>7RV</scope><scope>7T2</scope><scope>7T7</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2O</scope><scope>M7N</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-2570-3864</orcidid><orcidid>https://orcid.org/0000-0002-7171-732X</orcidid></search><sort><creationdate>202301</creationdate><title>Identifying type 1 and 2 diabetes in research datasets where classification biomarkers are unavailable: assessing the accuracy of published approaches</title><author>Thomas, Nicholas J. ; McGovern, Andrew ; Young, Katherine G. ; Sharp, Seth A. ; Weedon, Michael N. ; Hattersley, Andrew T. ; Dennis, John ; Jones, Angus G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c444t-781129122e8b47d98b9439be30968bc667a9faa213260f0546e6d9cea684e963</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Adult</topic><topic>Age</topic><topic>Availability</topic><topic>Biobanks</topic><topic>Biomarkers</topic><topic>Body image</topic><topic>Body mass index</topic><topic>Classification</topic><topic>Cohort stratification</topic><topic>Datasets</topic><topic>Diabetes</topic><topic>Diabetes classification</topic><topic>Diabetes epidemiology</topic><topic>Diabetes mellitus (insulin dependent)</topic><topic>Diabetes mellitus (non-insulin dependent)</topic><topic>Diabetes Mellitus, Type 1 - drug therapy</topic><topic>Diabetes Mellitus, Type 1 - genetics</topic><topic>Diabetes Mellitus, Type 2 - diagnosis</topic><topic>Diabetes Mellitus, Type 2 - drug therapy</topic><topic>Diabetes Mellitus, Type 2 - genetics</topic><topic>Diagnosis</topic><topic>Ethnicity</topic><topic>Humans</topic><topic>Insulin</topic><topic>Insulin - therapeutic use</topic><topic>Peptides</topic><topic>Population studies</topic><topic>Primary care</topic><topic>Risk Factors</topic><topic>Statistical analysis</topic><topic>Type 1 diabetes</topic><topic>Type 2 diabetes</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Thomas, Nicholas J.</creatorcontrib><creatorcontrib>McGovern, Andrew</creatorcontrib><creatorcontrib>Young, Katherine G.</creatorcontrib><creatorcontrib>Sharp, Seth A.</creatorcontrib><creatorcontrib>Weedon, Michael N.</creatorcontrib><creatorcontrib>Hattersley, Andrew T.</creatorcontrib><creatorcontrib>Dennis, John</creatorcontrib><creatorcontrib>Jones, Angus G.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</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 Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of clinical epidemiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Thomas, Nicholas J.</au><au>McGovern, Andrew</au><au>Young, Katherine G.</au><au>Sharp, Seth A.</au><au>Weedon, Michael N.</au><au>Hattersley, Andrew T.</au><au>Dennis, John</au><au>Jones, Angus G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identifying type 1 and 2 diabetes in research datasets where classification biomarkers are unavailable: assessing the accuracy of published approaches</atitle><jtitle>Journal of clinical epidemiology</jtitle><addtitle>J Clin Epidemiol</addtitle><date>2023-01</date><risdate>2023</risdate><volume>153</volume><spage>34</spage><epage>44</epage><pages>34-44</pages><issn>0895-4356</issn><eissn>1878-5921</eissn><abstract>We aimed to compare the performance of approaches for classifying insulin-treated diabetes within research datasets without measured classification biomarkers, evaluated against two independent biological definitions of diabetes type.
We compared accuracy of ten reported approaches for classifying insulin-treated diabetes into type 1 (T1D) and type 2 (T2D) diabetes in two cohorts: UK Biobank (UKBB) n = 26,399 and Diabetes Alliance for Research in England (DARE) n = 1,296. The overall performance for classifying T1D and T2D was assessed using: a T1D genetic risk score and genetic stratification method (UKBB); C-peptide measured at >3 years diabetes duration (DARE).
Approaches’ accuracy ranged from 71% to 88% (UKBB) and 68% to 88% (DARE). When classifying all participants, combining early insulin requirement with a T1D probability model (incorporating diagnosis age and body image issue [BMI]), and interview-reported diabetes type (UKBB available in only 15%) consistently achieved high accuracy (UKBB 87% and 87% and DARE 85% and 88%, respectively). For identifying T1D with minimal misclassification, models with high thresholds or young diagnosis age (<20 years) had highest performance. Findings were incorporated into an online tool identifying optimum approaches based on variable availability.
Models combining continuous features with early insulin requirement are the most accurate methods for classifying insulin-treated diabetes in research datasets without measured classification biomarkers.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>36368478</pmid><doi>10.1016/j.jclinepi.2022.10.022</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-2570-3864</orcidid><orcidid>https://orcid.org/0000-0002-7171-732X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Adult Age Availability Biobanks Biomarkers Body image Body mass index Classification Cohort stratification Datasets Diabetes Diabetes classification Diabetes epidemiology Diabetes mellitus (insulin dependent) Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 1 - drug therapy Diabetes Mellitus, Type 1 - genetics Diabetes Mellitus, Type 2 - diagnosis Diabetes Mellitus, Type 2 - drug therapy Diabetes Mellitus, Type 2 - genetics Diagnosis Ethnicity Humans Insulin Insulin - therapeutic use Peptides Population studies Primary care Risk Factors Statistical analysis Type 1 diabetes Type 2 diabetes Young Adult |
| title | Identifying type 1 and 2 diabetes in research datasets where classification biomarkers are unavailable: assessing the accuracy of published approaches |
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