Clinical Characteristics and Associated Risk Factors of Prediabetes in the Southwestern Region of Korea from 2010-2019
This study investigated the clinical characteristics and associated risk factors of prediabetes in the southwestern region of Korea. A total of 323 subjects from 13 prediabetes studies were included in the data analysis. Subjects with prediabetes were divided into the following subtypes: (1) normal...
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| Veröffentlicht in: | Journal of clinical medicine 2020-04, Vol.9 (4), p.1114 |
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| description | This study investigated the clinical characteristics and associated risk factors of prediabetes in the southwestern region of Korea. A total of 323 subjects from 13 prediabetes studies were included in the data analysis. Subjects with prediabetes were divided into the following subtypes: (1) normal glucose tolerance (NGT) with HbA1c 5.7%-6.4%; (2) isolated impaired fasting glucose (I-IFG); (3) isolated impaired glucose tolerance (I-IGT); and (4) combined I-IFG and I-IGT (C-IFG/IGT). Clinical and biochemical variables were compared among subtypes, and multivariate logistic regression analysis was used to identify risk factors for prediabetes subtypes. The overall proportion of subjects with NGT, I-IFG, I-IGT and C-IFG/IGT was 8.4%, 20.7%, 33.1% and 37.8%, respectively. In men, C-IFG/IGT was the most common subtype, while in women, I-IGT was the most common. The parameters related to dysglycemia, atherosclerosis and liver dysfunction were higher in subjects in the C-IFG/IGT subtype than in other subtypes. Multiple linear regression analysis revealed independent risk factors for increased FPG, 2h-PPG and HbA1c levels. This study identified the clinical features and independent risk factors for prediabetes subtypes. |
| doi_str_mv | 10.3390/jcm9041114 |
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A total of 323 subjects from 13 prediabetes studies were included in the data analysis. Subjects with prediabetes were divided into the following subtypes: (1) normal glucose tolerance (NGT) with HbA1c 5.7%-6.4%; (2) isolated impaired fasting glucose (I-IFG); (3) isolated impaired glucose tolerance (I-IGT); and (4) combined I-IFG and I-IGT (C-IFG/IGT). Clinical and biochemical variables were compared among subtypes, and multivariate logistic regression analysis was used to identify risk factors for prediabetes subtypes. The overall proportion of subjects with NGT, I-IFG, I-IGT and C-IFG/IGT was 8.4%, 20.7%, 33.1% and 37.8%, respectively. In men, C-IFG/IGT was the most common subtype, while in women, I-IGT was the most common. The parameters related to dysglycemia, atherosclerosis and liver dysfunction were higher in subjects in the C-IFG/IGT subtype than in other subtypes. Multiple linear regression analysis revealed independent risk factors for increased FPG, 2h-PPG and HbA1c levels. This study identified the clinical features and independent risk factors for prediabetes subtypes.</description><identifier>ISSN: 2077-0383</identifier><identifier>EISSN: 2077-0383</identifier><identifier>DOI: 10.3390/jcm9041114</identifier><identifier>PMID: 32295016</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Apolipoproteins ; Blood pressure ; Body mass index ; Cholesterol ; Clinical medicine ; Clinical trials ; Diabetes ; Disease ; Glucose ; Hemoglobin ; High density lipoprotein ; Insulin resistance ; Laboratories ; Metabolism ; Overweight ; Peptides ; Plasma ; Regression analysis ; Risk factors ; Variables ; Variance analysis ; Womens health</subject><ispartof>Journal of clinical medicine, 2020-04, Vol.9 (4), p.1114</ispartof><rights>2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 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A total of 323 subjects from 13 prediabetes studies were included in the data analysis. Subjects with prediabetes were divided into the following subtypes: (1) normal glucose tolerance (NGT) with HbA1c 5.7%-6.4%; (2) isolated impaired fasting glucose (I-IFG); (3) isolated impaired glucose tolerance (I-IGT); and (4) combined I-IFG and I-IGT (C-IFG/IGT). Clinical and biochemical variables were compared among subtypes, and multivariate logistic regression analysis was used to identify risk factors for prediabetes subtypes. The overall proportion of subjects with NGT, I-IFG, I-IGT and C-IFG/IGT was 8.4%, 20.7%, 33.1% and 37.8%, respectively. In men, C-IFG/IGT was the most common subtype, while in women, I-IGT was the most common. The parameters related to dysglycemia, atherosclerosis and liver dysfunction were higher in subjects in the C-IFG/IGT subtype than in other subtypes. Multiple linear regression analysis revealed independent risk factors for increased FPG, 2h-PPG and HbA1c levels. This study identified the clinical features and independent risk factors for prediabetes subtypes.</description><subject>Apolipoproteins</subject><subject>Blood pressure</subject><subject>Body mass index</subject><subject>Cholesterol</subject><subject>Clinical medicine</subject><subject>Clinical trials</subject><subject>Diabetes</subject><subject>Disease</subject><subject>Glucose</subject><subject>Hemoglobin</subject><subject>High density lipoprotein</subject><subject>Insulin resistance</subject><subject>Laboratories</subject><subject>Metabolism</subject><subject>Overweight</subject><subject>Peptides</subject><subject>Plasma</subject><subject>Regression analysis</subject><subject>Risk factors</subject><subject>Variables</subject><subject>Variance analysis</subject><subject>Womens health</subject><issn>2077-0383</issn><issn>2077-0383</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpdkV1rFTEQhoMottTe-AMk4E0RVvO52dwI5WA_sKC09TpMs7M9Oe4mNdmt-O-bQ2ttnYuZgXl4eYeXkLecfZTSsk8bP1mmOOfqBdkVzJiGyU6-fLLvkP1SNqxW1ynBzWuyI4WwmvF2l9yuxhCDh5Gu1pDBz5hDmYMvFGJPD0tJPsCMPT0P5Sc9qkDKhaaBfs_YB7jCGQsNkc5rpBdpmde_sVSNSM_xOqS4Jb-mjECHnCYqGGdNbfYNeTXAWHD_Ye6RH0dfLlcnzdm349PV4VnjlRFz03tltR0GaLmEfpCat7rVSiiLoJGjUV60g7UWDNfAei6tMUKxzkvRMy_kHvl8r3uzXE3Ye4xzhtHd5DBB_uMSBPf8EsPaXadbZ4RkWvIqcPAgkNOvpf7mplA8jiNETEtxombQKmPMFn3_H7pJS471PSdaxZnurN5SH-4pn1MpGYdHM5y5baLuX6IVfvfU_iP6Nz95B0hZmnE</recordid><startdate>20200413</startdate><enddate>20200413</enddate><creator>Oh, Mi-Ra</creator><creator>Jung, Su-Jin</creator><creator>Bae, Eun-Ju</creator><creator>Park, Byung-Hyun</creator><creator>Chae, Soo-Wan</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</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>5PM</scope><orcidid>https://orcid.org/0000-0002-8843-916X</orcidid></search><sort><creationdate>20200413</creationdate><title>Clinical Characteristics and Associated Risk Factors of Prediabetes in the Southwestern Region of Korea from 2010-2019</title><author>Oh, Mi-Ra ; Jung, Su-Jin ; Bae, Eun-Ju ; Park, Byung-Hyun ; Chae, Soo-Wan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c472t-dc4959ffa613adf35165654249ea5e1e74c26f999a715a0d139772408c32d0c23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Apolipoproteins</topic><topic>Blood pressure</topic><topic>Body mass index</topic><topic>Cholesterol</topic><topic>Clinical medicine</topic><topic>Clinical trials</topic><topic>Diabetes</topic><topic>Disease</topic><topic>Glucose</topic><topic>Hemoglobin</topic><topic>High density lipoprotein</topic><topic>Insulin resistance</topic><topic>Laboratories</topic><topic>Metabolism</topic><topic>Overweight</topic><topic>Peptides</topic><topic>Plasma</topic><topic>Regression analysis</topic><topic>Risk factors</topic><topic>Variables</topic><topic>Variance analysis</topic><topic>Womens health</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Oh, Mi-Ra</creatorcontrib><creatorcontrib>Jung, Su-Jin</creatorcontrib><creatorcontrib>Bae, Eun-Ju</creatorcontrib><creatorcontrib>Park, Byung-Hyun</creatorcontrib><creatorcontrib>Chae, Soo-Wan</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</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>PubMed Central (Full Participant titles)</collection><jtitle>Journal of clinical medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Oh, Mi-Ra</au><au>Jung, Su-Jin</au><au>Bae, Eun-Ju</au><au>Park, Byung-Hyun</au><au>Chae, Soo-Wan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Clinical Characteristics and Associated Risk Factors of Prediabetes in the Southwestern Region of Korea from 2010-2019</atitle><jtitle>Journal of clinical medicine</jtitle><addtitle>J Clin Med</addtitle><date>2020-04-13</date><risdate>2020</risdate><volume>9</volume><issue>4</issue><spage>1114</spage><pages>1114-</pages><issn>2077-0383</issn><eissn>2077-0383</eissn><abstract>This study investigated the clinical characteristics and associated risk factors of prediabetes in the southwestern region of Korea. A total of 323 subjects from 13 prediabetes studies were included in the data analysis. Subjects with prediabetes were divided into the following subtypes: (1) normal glucose tolerance (NGT) with HbA1c 5.7%-6.4%; (2) isolated impaired fasting glucose (I-IFG); (3) isolated impaired glucose tolerance (I-IGT); and (4) combined I-IFG and I-IGT (C-IFG/IGT). Clinical and biochemical variables were compared among subtypes, and multivariate logistic regression analysis was used to identify risk factors for prediabetes subtypes. The overall proportion of subjects with NGT, I-IFG, I-IGT and C-IFG/IGT was 8.4%, 20.7%, 33.1% and 37.8%, respectively. In men, C-IFG/IGT was the most common subtype, while in women, I-IGT was the most common. The parameters related to dysglycemia, atherosclerosis and liver dysfunction were higher in subjects in the C-IFG/IGT subtype than in other subtypes. Multiple linear regression analysis revealed independent risk factors for increased FPG, 2h-PPG and HbA1c levels. This study identified the clinical features and independent risk factors for prediabetes subtypes.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>32295016</pmid><doi>10.3390/jcm9041114</doi><orcidid>https://orcid.org/0000-0002-8843-916X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Apolipoproteins Blood pressure Body mass index Cholesterol Clinical medicine Clinical trials Diabetes Disease Glucose Hemoglobin High density lipoprotein Insulin resistance Laboratories Metabolism Overweight Peptides Plasma Regression analysis Risk factors Variables Variance analysis Womens health |
| title | Clinical Characteristics and Associated Risk Factors of Prediabetes in the Southwestern Region of Korea from 2010-2019 |
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