Comparing glycemic traits in defining diabetes among rural Chinese older adults

We sought to identify the optimal cut-off of glycated hemoglobin (HbA1c) for defining diabetes and to assess the agreements of fasting plasma glucose (FPG), fasting serum glucose (FSG), and HbA1c in defining diabetes among rural older adults in China. This population-based cross-sectional study incl...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:PloS one 2024-01, Vol.19 (1), p.e0296694
Hauptverfasser: Wang, Pin, Li, Yuanjing, Wang, Mingqi, Song, Lin, Dong, Yi, Han, Xiaolei, Tuomilehto, Jaakko, Wang, Yongxiang, Du, Yifeng, Qiu, Chengxuan
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 1
container_start_page e0296694
container_title PloS one
container_volume 19
creator Wang, Pin
Li, Yuanjing
Wang, Mingqi
Song, Lin
Dong, Yi
Han, Xiaolei
Tuomilehto, Jaakko
Wang, Yongxiang
Du, Yifeng
Qiu, Chengxuan
description We sought to identify the optimal cut-off of glycated hemoglobin (HbA1c) for defining diabetes and to assess the agreements of fasting plasma glucose (FPG), fasting serum glucose (FSG), and HbA1c in defining diabetes among rural older adults in China. This population-based cross-sectional study included 3547 participants (age ≥61 years, 57.8% women) from the Multidomain Interventions to Delay Dementia and Disability in Rural China from 2018-2019; of these, 3122 had no previously diagnosed diabetes. We identified the optimal cut-off of HbA1c against FPG ≥7.0 mmol/L for defining diabetes by using receiver operating characteristic curve and Youden index. The agreements of FPG, FSG, and HbA1c in defining diabetes were assessed using kappa statistics. Among participants without previously diagnosed diabetes (n = 3122), the optimal HbA1c cut-off for defining diabetes was 6.5% (48 mmol/mol), with the sensitivity of 88.9%, specificity of 93.7%, and Youden index of 0.825. The correlation coefficients were 0.845 between FPG and FSG, 0.574 between FPG and HbA1c, and 0.529 between FSG and HbA1c in the total sample (n = 3547). The kappa statistic for defining diabetes was 0.962 between FSG and FPG, and 0.812 between HbA1c and FPG. The optimal cut-off of HbA1c for diagnosing diabetes against FPG >7.0 mmol/L is ≥6.5% in Chinese rural-dwelling older adults. The agreement in defining diabetes using FPG, FSG, and HbA1c is nearly perfect. These results have relevant implications for diabetes research and clinical practice among older adults in China. The protocol of MIND-China was registered in the Chinese Clinical Trial Registry (ChiCTR, www.chictr.org.cn; registration no.: ChiCTR1800017758).
doi_str_mv 10.1371/journal.pone.0296694
format Article
fullrecord <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_3069269701</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A780401166</galeid><doaj_id>oai_doaj_org_article_3ca7ffbb73ba47288bc9562cf178b068</doaj_id><sourcerecordid>A780401166</sourcerecordid><originalsourceid>FETCH-LOGICAL-c768t-d2754f37718c4b1d575ce50682948064e0ed8d51de41784a66f42e70749733e83</originalsourceid><addsrcrecordid>eNqNk2uL1DAUhoso7rr6D0QLgig4Y25N0k8yjLeFhQUv-zWk6elMxkwzm7Tq_nvTne4ylRWkH5qePO97Tk9ysuwpRnNMBX678X1otZvvfAtzRErOS3YvO8YlJTNOEL1_sD7KHsW4QaigkvOH2RGVRCQTdpydL_12p4NtV_nKXRnYWpN3Qdsu5rbNa2hsO-zVVlfQQcz11qfP0Aft8uXathAh966GkOu6d118nD1otIvwZHyfZN8_fvi2_Dw7O_90ulyczYzgspvVRBSsoUJgaViF60IUBgrEJSmZRJwBglrWBa6BYSGZ5rxhBAQSrBSUgqQn2fO97875qMZeREURLwkvBcKJON0TtdcbtQt2q8OV8tqq64APK6VDZ40DRY0WTVNVglaaCSJlZcqCE9Ok3FUqKnnN9l7xF-z6auI2hn6kFSjJ6FDhSfbmn_x7e7G4zh57RYigJUn4u_Fn-moLtYE2HYGbqKY7rV2rlf-pMJIYMTIU-Gp0CP6yh9iprY0GnNMt-D4qUuJSMI5lkdAXf6F3d2-kVjo1yLaNT4nNYKoWQiKGMOY8UfM7qPTUw0VKF7OxKT4RvJ4IEtPB726l-xjV6dcv_8-eX0zZlwfsGrTr1tG7vrO-jVOQ7UETfIwBmtsuY6SGubrphhrmSo1zlWTPDk_oVnQzSPQPMH4cRg</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3069269701</pqid></control><display><type>article</type><title>Comparing glycemic traits in defining diabetes among rural Chinese older adults</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central</source><source>SWEPUB Freely available online</source><source>Free Full-Text Journals in Chemistry</source><source>Public Library of Science (PLoS)</source><creator>Wang, Pin ; Li, Yuanjing ; Wang, Mingqi ; Song, Lin ; Dong, Yi ; Han, Xiaolei ; Tuomilehto, Jaakko ; Wang, Yongxiang ; Du, Yifeng ; Qiu, Chengxuan</creator><contributor>mashili, Fredirick Lazaro</contributor><creatorcontrib>Wang, Pin ; Li, Yuanjing ; Wang, Mingqi ; Song, Lin ; Dong, Yi ; Han, Xiaolei ; Tuomilehto, Jaakko ; Wang, Yongxiang ; Du, Yifeng ; Qiu, Chengxuan ; mashili, Fredirick Lazaro</creatorcontrib><description>We sought to identify the optimal cut-off of glycated hemoglobin (HbA1c) for defining diabetes and to assess the agreements of fasting plasma glucose (FPG), fasting serum glucose (FSG), and HbA1c in defining diabetes among rural older adults in China. This population-based cross-sectional study included 3547 participants (age ≥61 years, 57.8% women) from the Multidomain Interventions to Delay Dementia and Disability in Rural China from 2018-2019; of these, 3122 had no previously diagnosed diabetes. We identified the optimal cut-off of HbA1c against FPG ≥7.0 mmol/L for defining diabetes by using receiver operating characteristic curve and Youden index. The agreements of FPG, FSG, and HbA1c in defining diabetes were assessed using kappa statistics. Among participants without previously diagnosed diabetes (n = 3122), the optimal HbA1c cut-off for defining diabetes was 6.5% (48 mmol/mol), with the sensitivity of 88.9%, specificity of 93.7%, and Youden index of 0.825. The correlation coefficients were 0.845 between FPG and FSG, 0.574 between FPG and HbA1c, and 0.529 between FSG and HbA1c in the total sample (n = 3547). The kappa statistic for defining diabetes was 0.962 between FSG and FPG, and 0.812 between HbA1c and FPG. The optimal cut-off of HbA1c for diagnosing diabetes against FPG &gt;7.0 mmol/L is ≥6.5% in Chinese rural-dwelling older adults. The agreement in defining diabetes using FPG, FSG, and HbA1c is nearly perfect. These results have relevant implications for diabetes research and clinical practice among older adults in China. The protocol of MIND-China was registered in the Chinese Clinical Trial Registry (ChiCTR, www.chictr.org.cn; registration no.: ChiCTR1800017758).</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0296694</identifier><identifier>PMID: 38271374</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adults ; Aged ; Alcohol use ; Biology and life sciences ; Blood Glucose ; Blood pressure ; Cardiovascular disease ; Care and treatment ; China - epidemiology ; Cholesterol ; Correlation coefficient ; Correlation coefficients ; Cross-Sectional Studies ; Dementia ; Dementia disorders ; Diabetes ; Diabetes in old age ; Diabetes mellitus ; Diabetes Mellitus - diagnosis ; Diabetes Mellitus - epidemiology ; Diagnosis ; Drugs ; Evaluation ; Exercise ; Fasting ; Female ; Fluorides ; Glucose ; Glycated Hemoglobin ; Glycemic index ; Health aspects ; Hemoglobin ; Humans ; Hyperglycemia ; Hypertension ; Laboratories ; Male ; Medical diagnosis ; Medical tests ; Medicine and Health Sciences ; Mental disorders ; Middle Aged ; Older people ; People and Places ; Physical Sciences ; Plasma ; Population studies ; Population-based studies ; Self report</subject><ispartof>PloS one, 2024-01, Vol.19 (1), p.e0296694</ispartof><rights>Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2024 Public Library of Science</rights><rights>2024 Wang 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>2024 Wang et al 2024 Wang et al</rights><rights>2024 Wang 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><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c768t-d2754f37718c4b1d575ce50682948064e0ed8d51de41784a66f42e70749733e83</citedby><cites>FETCH-LOGICAL-c768t-d2754f37718c4b1d575ce50682948064e0ed8d51de41784a66f42e70749733e83</cites><orcidid>0000-0002-4672-6654</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/PMC10810428/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10810428/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,550,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38271374$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-227392$$DView record from Swedish Publication Index$$Hfree_for_read</backlink><backlink>$$Uhttp://kipublications.ki.se/Default.aspx?queryparsed=id:154948666$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><contributor>mashili, Fredirick Lazaro</contributor><creatorcontrib>Wang, Pin</creatorcontrib><creatorcontrib>Li, Yuanjing</creatorcontrib><creatorcontrib>Wang, Mingqi</creatorcontrib><creatorcontrib>Song, Lin</creatorcontrib><creatorcontrib>Dong, Yi</creatorcontrib><creatorcontrib>Han, Xiaolei</creatorcontrib><creatorcontrib>Tuomilehto, Jaakko</creatorcontrib><creatorcontrib>Wang, Yongxiang</creatorcontrib><creatorcontrib>Du, Yifeng</creatorcontrib><creatorcontrib>Qiu, Chengxuan</creatorcontrib><title>Comparing glycemic traits in defining diabetes among rural Chinese older adults</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>We sought to identify the optimal cut-off of glycated hemoglobin (HbA1c) for defining diabetes and to assess the agreements of fasting plasma glucose (FPG), fasting serum glucose (FSG), and HbA1c in defining diabetes among rural older adults in China. This population-based cross-sectional study included 3547 participants (age ≥61 years, 57.8% women) from the Multidomain Interventions to Delay Dementia and Disability in Rural China from 2018-2019; of these, 3122 had no previously diagnosed diabetes. We identified the optimal cut-off of HbA1c against FPG ≥7.0 mmol/L for defining diabetes by using receiver operating characteristic curve and Youden index. The agreements of FPG, FSG, and HbA1c in defining diabetes were assessed using kappa statistics. Among participants without previously diagnosed diabetes (n = 3122), the optimal HbA1c cut-off for defining diabetes was 6.5% (48 mmol/mol), with the sensitivity of 88.9%, specificity of 93.7%, and Youden index of 0.825. The correlation coefficients were 0.845 between FPG and FSG, 0.574 between FPG and HbA1c, and 0.529 between FSG and HbA1c in the total sample (n = 3547). The kappa statistic for defining diabetes was 0.962 between FSG and FPG, and 0.812 between HbA1c and FPG. The optimal cut-off of HbA1c for diagnosing diabetes against FPG &gt;7.0 mmol/L is ≥6.5% in Chinese rural-dwelling older adults. The agreement in defining diabetes using FPG, FSG, and HbA1c is nearly perfect. These results have relevant implications for diabetes research and clinical practice among older adults in China. The protocol of MIND-China was registered in the Chinese Clinical Trial Registry (ChiCTR, www.chictr.org.cn; registration no.: ChiCTR1800017758).</description><subject>Adults</subject><subject>Aged</subject><subject>Alcohol use</subject><subject>Biology and life sciences</subject><subject>Blood Glucose</subject><subject>Blood pressure</subject><subject>Cardiovascular disease</subject><subject>Care and treatment</subject><subject>China - epidemiology</subject><subject>Cholesterol</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Cross-Sectional Studies</subject><subject>Dementia</subject><subject>Dementia disorders</subject><subject>Diabetes</subject><subject>Diabetes in old age</subject><subject>Diabetes mellitus</subject><subject>Diabetes Mellitus - diagnosis</subject><subject>Diabetes Mellitus - epidemiology</subject><subject>Diagnosis</subject><subject>Drugs</subject><subject>Evaluation</subject><subject>Exercise</subject><subject>Fasting</subject><subject>Female</subject><subject>Fluorides</subject><subject>Glucose</subject><subject>Glycated Hemoglobin</subject><subject>Glycemic index</subject><subject>Health aspects</subject><subject>Hemoglobin</subject><subject>Humans</subject><subject>Hyperglycemia</subject><subject>Hypertension</subject><subject>Laboratories</subject><subject>Male</subject><subject>Medical diagnosis</subject><subject>Medical tests</subject><subject>Medicine and Health Sciences</subject><subject>Mental disorders</subject><subject>Middle Aged</subject><subject>Older people</subject><subject>People and Places</subject><subject>Physical Sciences</subject><subject>Plasma</subject><subject>Population studies</subject><subject>Population-based studies</subject><subject>Self report</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>D8T</sourceid><sourceid>DOA</sourceid><recordid>eNqNk2uL1DAUhoso7rr6D0QLgig4Y25N0k8yjLeFhQUv-zWk6elMxkwzm7Tq_nvTne4ylRWkH5qePO97Tk9ysuwpRnNMBX678X1otZvvfAtzRErOS3YvO8YlJTNOEL1_sD7KHsW4QaigkvOH2RGVRCQTdpydL_12p4NtV_nKXRnYWpN3Qdsu5rbNa2hsO-zVVlfQQcz11qfP0Aft8uXathAh966GkOu6d118nD1otIvwZHyfZN8_fvi2_Dw7O_90ulyczYzgspvVRBSsoUJgaViF60IUBgrEJSmZRJwBglrWBa6BYSGZ5rxhBAQSrBSUgqQn2fO97875qMZeREURLwkvBcKJON0TtdcbtQt2q8OV8tqq64APK6VDZ40DRY0WTVNVglaaCSJlZcqCE9Ok3FUqKnnN9l7xF-z6auI2hn6kFSjJ6FDhSfbmn_x7e7G4zh57RYigJUn4u_Fn-moLtYE2HYGbqKY7rV2rlf-pMJIYMTIU-Gp0CP6yh9iprY0GnNMt-D4qUuJSMI5lkdAXf6F3d2-kVjo1yLaNT4nNYKoWQiKGMOY8UfM7qPTUw0VKF7OxKT4RvJ4IEtPB726l-xjV6dcv_8-eX0zZlwfsGrTr1tG7vrO-jVOQ7UETfIwBmtsuY6SGubrphhrmSo1zlWTPDk_oVnQzSPQPMH4cRg</recordid><startdate>20240125</startdate><enddate>20240125</enddate><creator>Wang, Pin</creator><creator>Li, Yuanjing</creator><creator>Wang, Mingqi</creator><creator>Song, Lin</creator><creator>Dong, Yi</creator><creator>Han, Xiaolei</creator><creator>Tuomilehto, Jaakko</creator><creator>Wang, Yongxiang</creator><creator>Du, Yifeng</creator><creator>Qiu, Chengxuan</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>IOV</scope><scope>ISR</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>AEUYN</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>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>ABAVF</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D8T</scope><scope>DG7</scope><scope>ZZAVC</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4672-6654</orcidid></search><sort><creationdate>20240125</creationdate><title>Comparing glycemic traits in defining diabetes among rural Chinese older adults</title><author>Wang, Pin ; Li, Yuanjing ; Wang, Mingqi ; Song, Lin ; Dong, Yi ; Han, Xiaolei ; Tuomilehto, Jaakko ; Wang, Yongxiang ; Du, Yifeng ; Qiu, Chengxuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c768t-d2754f37718c4b1d575ce50682948064e0ed8d51de41784a66f42e70749733e83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adults</topic><topic>Aged</topic><topic>Alcohol use</topic><topic>Biology and life sciences</topic><topic>Blood Glucose</topic><topic>Blood pressure</topic><topic>Cardiovascular disease</topic><topic>Care and treatment</topic><topic>China - epidemiology</topic><topic>Cholesterol</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Cross-Sectional Studies</topic><topic>Dementia</topic><topic>Dementia disorders</topic><topic>Diabetes</topic><topic>Diabetes in old age</topic><topic>Diabetes mellitus</topic><topic>Diabetes Mellitus - diagnosis</topic><topic>Diabetes Mellitus - epidemiology</topic><topic>Diagnosis</topic><topic>Drugs</topic><topic>Evaluation</topic><topic>Exercise</topic><topic>Fasting</topic><topic>Female</topic><topic>Fluorides</topic><topic>Glucose</topic><topic>Glycated Hemoglobin</topic><topic>Glycemic index</topic><topic>Health aspects</topic><topic>Hemoglobin</topic><topic>Humans</topic><topic>Hyperglycemia</topic><topic>Hypertension</topic><topic>Laboratories</topic><topic>Male</topic><topic>Medical diagnosis</topic><topic>Medical tests</topic><topic>Medicine and Health Sciences</topic><topic>Mental disorders</topic><topic>Middle Aged</topic><topic>Older people</topic><topic>People and Places</topic><topic>Physical Sciences</topic><topic>Plasma</topic><topic>Population studies</topic><topic>Population-based studies</topic><topic>Self report</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Pin</creatorcontrib><creatorcontrib>Li, Yuanjing</creatorcontrib><creatorcontrib>Wang, Mingqi</creatorcontrib><creatorcontrib>Song, Lin</creatorcontrib><creatorcontrib>Dong, Yi</creatorcontrib><creatorcontrib>Han, Xiaolei</creatorcontrib><creatorcontrib>Tuomilehto, Jaakko</creatorcontrib><creatorcontrib>Wang, Yongxiang</creatorcontrib><creatorcontrib>Du, Yifeng</creatorcontrib><creatorcontrib>Qiu, Chengxuan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing &amp; Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</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>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</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>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>SWEPUB Stockholms universitet full text</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Freely available online</collection><collection>SWEPUB Stockholms universitet</collection><collection>SwePub Articles full text</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>Wang, Pin</au><au>Li, Yuanjing</au><au>Wang, Mingqi</au><au>Song, Lin</au><au>Dong, Yi</au><au>Han, Xiaolei</au><au>Tuomilehto, Jaakko</au><au>Wang, Yongxiang</au><au>Du, Yifeng</au><au>Qiu, Chengxuan</au><au>mashili, Fredirick Lazaro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparing glycemic traits in defining diabetes among rural Chinese older adults</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2024-01-25</date><risdate>2024</risdate><volume>19</volume><issue>1</issue><spage>e0296694</spage><pages>e0296694-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>We sought to identify the optimal cut-off of glycated hemoglobin (HbA1c) for defining diabetes and to assess the agreements of fasting plasma glucose (FPG), fasting serum glucose (FSG), and HbA1c in defining diabetes among rural older adults in China. This population-based cross-sectional study included 3547 participants (age ≥61 years, 57.8% women) from the Multidomain Interventions to Delay Dementia and Disability in Rural China from 2018-2019; of these, 3122 had no previously diagnosed diabetes. We identified the optimal cut-off of HbA1c against FPG ≥7.0 mmol/L for defining diabetes by using receiver operating characteristic curve and Youden index. The agreements of FPG, FSG, and HbA1c in defining diabetes were assessed using kappa statistics. Among participants without previously diagnosed diabetes (n = 3122), the optimal HbA1c cut-off for defining diabetes was 6.5% (48 mmol/mol), with the sensitivity of 88.9%, specificity of 93.7%, and Youden index of 0.825. The correlation coefficients were 0.845 between FPG and FSG, 0.574 between FPG and HbA1c, and 0.529 between FSG and HbA1c in the total sample (n = 3547). The kappa statistic for defining diabetes was 0.962 between FSG and FPG, and 0.812 between HbA1c and FPG. The optimal cut-off of HbA1c for diagnosing diabetes against FPG &gt;7.0 mmol/L is ≥6.5% in Chinese rural-dwelling older adults. The agreement in defining diabetes using FPG, FSG, and HbA1c is nearly perfect. These results have relevant implications for diabetes research and clinical practice among older adults in China. The protocol of MIND-China was registered in the Chinese Clinical Trial Registry (ChiCTR, www.chictr.org.cn; registration no.: ChiCTR1800017758).</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>38271374</pmid><doi>10.1371/journal.pone.0296694</doi><tpages>e0296694</tpages><orcidid>https://orcid.org/0000-0002-4672-6654</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1932-6203
ispartof PloS one, 2024-01, Vol.19 (1), p.e0296694
issn 1932-6203
1932-6203
language eng
recordid cdi_plos_journals_3069269701
source MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; SWEPUB Freely available online; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS)
subjects Adults
Aged
Alcohol use
Biology and life sciences
Blood Glucose
Blood pressure
Cardiovascular disease
Care and treatment
China - epidemiology
Cholesterol
Correlation coefficient
Correlation coefficients
Cross-Sectional Studies
Dementia
Dementia disorders
Diabetes
Diabetes in old age
Diabetes mellitus
Diabetes Mellitus - diagnosis
Diabetes Mellitus - epidemiology
Diagnosis
Drugs
Evaluation
Exercise
Fasting
Female
Fluorides
Glucose
Glycated Hemoglobin
Glycemic index
Health aspects
Hemoglobin
Humans
Hyperglycemia
Hypertension
Laboratories
Male
Medical diagnosis
Medical tests
Medicine and Health Sciences
Mental disorders
Middle Aged
Older people
People and Places
Physical Sciences
Plasma
Population studies
Population-based studies
Self report
title Comparing glycemic traits in defining diabetes among rural Chinese older adults
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T21%3A34%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Comparing%20glycemic%20traits%20in%20defining%20diabetes%20among%20rural%20Chinese%20older%20adults&rft.jtitle=PloS%20one&rft.au=Wang,%20Pin&rft.date=2024-01-25&rft.volume=19&rft.issue=1&rft.spage=e0296694&rft.pages=e0296694-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0296694&rft_dat=%3Cgale_plos_%3EA780401166%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3069269701&rft_id=info:pmid/38271374&rft_galeid=A780401166&rft_doaj_id=oai_doaj_org_article_3ca7ffbb73ba47288bc9562cf178b068&rfr_iscdi=true