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Optimal Obesity- and Lipid-Related Indices for Predicting Metabolic Syndrome in Chronic Kidney Disease Patients with and without Type 2 Diabetes Mellitus in China
Existing obesity- and lipid-related indices are inconsistent with metabolic syndrome (MetS) in chronic kidney disease (CKD) patients. We compared seven indicators, including waist circumference (WC), body mass index (BMI), visceral fat area (VFA), subcutaneous fat area (SFA), visceral adiposity inde...
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Published in: | Nutrients 2022-03, Vol.14 (7), p.1334 |
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description | Existing obesity- and lipid-related indices are inconsistent with metabolic syndrome (MetS) in chronic kidney disease (CKD) patients. We compared seven indicators, including waist circumference (WC), body mass index (BMI), visceral fat area (VFA), subcutaneous fat area (SFA), visceral adiposity index (VAI), Chinese VAI and lipid accumulation product (LAP), to evaluate their ability to predict MetS in CKD patients with and without Type 2 diabetes mellitus (T2DM) under various criteria. Multivariate logistic regression analysis was used to investigate the independent associations between the indices and metabolic syndrome among 547 non-dialysis CKD patients, aged ≥18 years. The predictive power of these indices was assessed using receiver operating characteristic (ROC) curve analysis. After adjusting for potential confounders, the correlation between VAI and MetS was strongest based on the optimal cut-off value of 1.51 (sensitivity 86.84%, specificity 91.18%) and 2.35 (sensitivity 83.54%, specificity 86.08%), with OR values of 40.585 (8.683-189.695) and 5.076 (1.247-20.657) for males and females with CKD and T2DM. In CKD patients without T2DM, based on the optimal cut-off values of 1.806 (sensitivity 98.11%, specificity 72.73%) and 3.11 (sensitivity 84.62%, specificity 83.82%), the OR values were 7.514 (3.757-15.027) and 3.008 (1.789-5.056) for males and females, respectively. The area under ROC curve (AUC) and Youden index of VAI were the highest among the seven indexes, indicating its superiority in predicting MetS in both male and female CKD patients, especially those with T2DM. |
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We compared seven indicators, including waist circumference (WC), body mass index (BMI), visceral fat area (VFA), subcutaneous fat area (SFA), visceral adiposity index (VAI), Chinese VAI and lipid accumulation product (LAP), to evaluate their ability to predict MetS in CKD patients with and without Type 2 diabetes mellitus (T2DM) under various criteria. Multivariate logistic regression analysis was used to investigate the independent associations between the indices and metabolic syndrome among 547 non-dialysis CKD patients, aged ≥18 years. The predictive power of these indices was assessed using receiver operating characteristic (ROC) curve analysis. After adjusting for potential confounders, the correlation between VAI and MetS was strongest based on the optimal cut-off value of 1.51 (sensitivity 86.84%, specificity 91.18%) and 2.35 (sensitivity 83.54%, specificity 86.08%), with OR values of 40.585 (8.683-189.695) and 5.076 (1.247-20.657) for males and females with CKD and T2DM. In CKD patients without T2DM, based on the optimal cut-off values of 1.806 (sensitivity 98.11%, specificity 72.73%) and 3.11 (sensitivity 84.62%, specificity 83.82%), the OR values were 7.514 (3.757-15.027) and 3.008 (1.789-5.056) for males and females, respectively. The area under ROC curve (AUC) and Youden index of VAI were the highest among the seven indexes, indicating its superiority in predicting MetS in both male and female CKD patients, especially those with T2DM.</description><identifier>ISSN: 2072-6643</identifier><identifier>EISSN: 2072-6643</identifier><identifier>DOI: 10.3390/nu14071334</identifier><identifier>PMID: 35405947</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Adipose tissue ; Adiposity ; Adolescent ; Adult ; Blood pressure ; Body mass ; Body Mass Index ; Body size ; Cardiovascular disease ; China - epidemiology ; Cholesterol ; chronic kidney disease ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus, Type 2 - complications ; Dialysis ; Fasting ; Female ; Glucose ; High density lipoprotein ; Humans ; Hyperglycemia ; Hypertension ; Kidney diseases ; Kidneys ; lipid accumulation product ; Lipids ; Lipoproteins ; Male ; Metabolic disorders ; Metabolic syndrome ; Metabolic Syndrome - complications ; Metabolic Syndrome - diagnosis ; Mortality ; Obesity ; Obesity - complications ; Obesity, Abdominal - complications ; Patients ; Peritoneal dialysis ; Regression analysis ; Renal Insufficiency, Chronic - complications ; Renal Insufficiency, Chronic - diagnosis ; ROC Curve ; Sensitivity ; Statistical analysis ; type 2 diabetes mellitus ; visceral adiposity index ; visceral fat area ; Waist Circumference</subject><ispartof>Nutrients, 2022-03, Vol.14 (7), p.1334</ispartof><rights>2022 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 (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 by the authors. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c472t-20b393af10614740ca75a79b63587a8f50c942fe2208a42ff3178eeec335a3623</citedby><cites>FETCH-LOGICAL-c472t-20b393af10614740ca75a79b63587a8f50c942fe2208a42ff3178eeec335a3623</cites><orcidid>0000-0002-4184-2699</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2649038089/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2649038089?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35405947$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Hangtian</creatorcontrib><creatorcontrib>Wang, Qian</creatorcontrib><creatorcontrib>Ke, Jianghua</creatorcontrib><creatorcontrib>Lin, Wenwen</creatorcontrib><creatorcontrib>Luo, Yayong</creatorcontrib><creatorcontrib>Yao, Jin</creatorcontrib><creatorcontrib>Zhang, Weiguang</creatorcontrib><creatorcontrib>Zhang, Li</creatorcontrib><creatorcontrib>Duan, Shuwei</creatorcontrib><creatorcontrib>Dong, Zheyi</creatorcontrib><creatorcontrib>Chen, Xiangmei</creatorcontrib><title>Optimal Obesity- and Lipid-Related Indices for Predicting Metabolic Syndrome in Chronic Kidney Disease Patients with and without Type 2 Diabetes Mellitus in China</title><title>Nutrients</title><addtitle>Nutrients</addtitle><description>Existing obesity- and lipid-related indices are inconsistent with metabolic syndrome (MetS) in chronic kidney disease (CKD) patients. We compared seven indicators, including waist circumference (WC), body mass index (BMI), visceral fat area (VFA), subcutaneous fat area (SFA), visceral adiposity index (VAI), Chinese VAI and lipid accumulation product (LAP), to evaluate their ability to predict MetS in CKD patients with and without Type 2 diabetes mellitus (T2DM) under various criteria. Multivariate logistic regression analysis was used to investigate the independent associations between the indices and metabolic syndrome among 547 non-dialysis CKD patients, aged ≥18 years. The predictive power of these indices was assessed using receiver operating characteristic (ROC) curve analysis. After adjusting for potential confounders, the correlation between VAI and MetS was strongest based on the optimal cut-off value of 1.51 (sensitivity 86.84%, specificity 91.18%) and 2.35 (sensitivity 83.54%, specificity 86.08%), with OR values of 40.585 (8.683-189.695) and 5.076 (1.247-20.657) for males and females with CKD and T2DM. In CKD patients without T2DM, based on the optimal cut-off values of 1.806 (sensitivity 98.11%, specificity 72.73%) and 3.11 (sensitivity 84.62%, specificity 83.82%), the OR values were 7.514 (3.757-15.027) and 3.008 (1.789-5.056) for males and females, respectively. The area under ROC curve (AUC) and Youden index of VAI were the highest among the seven indexes, indicating its superiority in predicting MetS in both male and female CKD patients, especially those with T2DM.</description><subject>Adipose tissue</subject><subject>Adiposity</subject><subject>Adolescent</subject><subject>Adult</subject><subject>Blood pressure</subject><subject>Body mass</subject><subject>Body Mass Index</subject><subject>Body size</subject><subject>Cardiovascular disease</subject><subject>China - epidemiology</subject><subject>Cholesterol</subject><subject>chronic kidney disease</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetes Mellitus, Type 2 - complications</subject><subject>Dialysis</subject><subject>Fasting</subject><subject>Female</subject><subject>Glucose</subject><subject>High density lipoprotein</subject><subject>Humans</subject><subject>Hyperglycemia</subject><subject>Hypertension</subject><subject>Kidney diseases</subject><subject>Kidneys</subject><subject>lipid accumulation product</subject><subject>Lipids</subject><subject>Lipoproteins</subject><subject>Male</subject><subject>Metabolic disorders</subject><subject>Metabolic syndrome</subject><subject>Metabolic Syndrome - complications</subject><subject>Metabolic Syndrome - diagnosis</subject><subject>Mortality</subject><subject>Obesity</subject><subject>Obesity - complications</subject><subject>Obesity, Abdominal - complications</subject><subject>Patients</subject><subject>Peritoneal dialysis</subject><subject>Regression analysis</subject><subject>Renal Insufficiency, Chronic - complications</subject><subject>Renal Insufficiency, Chronic - diagnosis</subject><subject>ROC Curve</subject><subject>Sensitivity</subject><subject>Statistical analysis</subject><subject>type 2 diabetes mellitus</subject><subject>visceral adiposity index</subject><subject>visceral fat area</subject><subject>Waist Circumference</subject><issn>2072-6643</issn><issn>2072-6643</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdks9u1DAQxiMEotXSCw-ALHFBSIGJ7cTJBQkt_1ZstRWUs-Ukk12vsnZqO6C8Dk-Ks1tKiy8ej3_6vvF4kuR5Bm8Yq-CtGTMOImOMP0rOKQiaFgVnj-_FZ8mF93uYlwBRsKfJGcs55BUX58nvzRD0QfVkU6PXYUqJMi1Z60G36TfsVcCWrEyrG_Sks45cOYyHoM2WXGJQte11Q75PpnX2gEQbstw5a2Luq24NTuSD9qg8kisVNJrgyS8ddkePObBjINfTgIRGUNUYossl9r0Ooz-JaaOeJU861Xu8uN0XyY9PH6-XX9L15vNq-X6dNlzQkFKoWcVUl0GRccGhUSJXoqoLlpdClV0OTcVph5RCqWLQsUyUiNgwlitWULZIVifd1qq9HFxsi5ukVVoeE9ZtpXJBNz1K3nZ1zRnWZc55VxRlzUooIfoJKEuAqPXupDWM9QHbJj7dqf6B6MMbo3dya3_KCoCy-GmL5NWtgLM3I_ogD9o3sTXKoB29pAWv8orm1ez18j90b0dnYquOFMylVZF6faIaZ7132N0Vk4GcJ0n-m6QIv7hf_h36d27YHzjpw3s</recordid><startdate>20220323</startdate><enddate>20220323</enddate><creator>Li, Hangtian</creator><creator>Wang, Qian</creator><creator>Ke, Jianghua</creator><creator>Lin, Wenwen</creator><creator>Luo, Yayong</creator><creator>Yao, Jin</creator><creator>Zhang, Weiguang</creator><creator>Zhang, Li</creator><creator>Duan, Shuwei</creator><creator>Dong, Zheyi</creator><creator>Chen, Xiangmei</creator><general>MDPI AG</general><general>MDPI</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>3V.</scope><scope>7TS</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</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>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4184-2699</orcidid></search><sort><creationdate>20220323</creationdate><title>Optimal Obesity- and Lipid-Related Indices for Predicting Metabolic Syndrome in Chronic Kidney Disease Patients with and without Type 2 Diabetes Mellitus in China</title><author>Li, Hangtian ; Wang, Qian ; Ke, Jianghua ; Lin, Wenwen ; Luo, Yayong ; Yao, Jin ; Zhang, Weiguang ; Zhang, Li ; Duan, Shuwei ; Dong, Zheyi ; Chen, Xiangmei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c472t-20b393af10614740ca75a79b63587a8f50c942fe2208a42ff3178eeec335a3623</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adipose tissue</topic><topic>Adiposity</topic><topic>Adolescent</topic><topic>Adult</topic><topic>Blood pressure</topic><topic>Body mass</topic><topic>Body Mass Index</topic><topic>Body size</topic><topic>Cardiovascular disease</topic><topic>China - epidemiology</topic><topic>Cholesterol</topic><topic>chronic kidney disease</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes mellitus (non-insulin dependent)</topic><topic>Diabetes Mellitus, Type 2 - complications</topic><topic>Dialysis</topic><topic>Fasting</topic><topic>Female</topic><topic>Glucose</topic><topic>High density lipoprotein</topic><topic>Humans</topic><topic>Hyperglycemia</topic><topic>Hypertension</topic><topic>Kidney diseases</topic><topic>Kidneys</topic><topic>lipid accumulation product</topic><topic>Lipids</topic><topic>Lipoproteins</topic><topic>Male</topic><topic>Metabolic disorders</topic><topic>Metabolic syndrome</topic><topic>Metabolic Syndrome - complications</topic><topic>Metabolic Syndrome - diagnosis</topic><topic>Mortality</topic><topic>Obesity</topic><topic>Obesity - complications</topic><topic>Obesity, Abdominal - complications</topic><topic>Patients</topic><topic>Peritoneal dialysis</topic><topic>Regression analysis</topic><topic>Renal Insufficiency, Chronic - complications</topic><topic>Renal Insufficiency, Chronic - diagnosis</topic><topic>ROC Curve</topic><topic>Sensitivity</topic><topic>Statistical analysis</topic><topic>type 2 diabetes mellitus</topic><topic>visceral adiposity index</topic><topic>visceral fat area</topic><topic>Waist Circumference</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Hangtian</creatorcontrib><creatorcontrib>Wang, Qian</creatorcontrib><creatorcontrib>Ke, Jianghua</creatorcontrib><creatorcontrib>Lin, Wenwen</creatorcontrib><creatorcontrib>Luo, Yayong</creatorcontrib><creatorcontrib>Yao, Jin</creatorcontrib><creatorcontrib>Zhang, Weiguang</creatorcontrib><creatorcontrib>Zhang, Li</creatorcontrib><creatorcontrib>Duan, Shuwei</creatorcontrib><creatorcontrib>Dong, Zheyi</creatorcontrib><creatorcontrib>Chen, Xiangmei</creatorcontrib><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>Physical Education Index</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</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)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Databases</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</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>Medical Database</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>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Nutrients</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Hangtian</au><au>Wang, Qian</au><au>Ke, Jianghua</au><au>Lin, Wenwen</au><au>Luo, Yayong</au><au>Yao, Jin</au><au>Zhang, Weiguang</au><au>Zhang, Li</au><au>Duan, Shuwei</au><au>Dong, Zheyi</au><au>Chen, Xiangmei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal Obesity- and Lipid-Related Indices for Predicting Metabolic Syndrome in Chronic Kidney Disease Patients with and without Type 2 Diabetes Mellitus in China</atitle><jtitle>Nutrients</jtitle><addtitle>Nutrients</addtitle><date>2022-03-23</date><risdate>2022</risdate><volume>14</volume><issue>7</issue><spage>1334</spage><pages>1334-</pages><issn>2072-6643</issn><eissn>2072-6643</eissn><abstract>Existing obesity- and lipid-related indices are inconsistent with metabolic syndrome (MetS) in chronic kidney disease (CKD) patients. We compared seven indicators, including waist circumference (WC), body mass index (BMI), visceral fat area (VFA), subcutaneous fat area (SFA), visceral adiposity index (VAI), Chinese VAI and lipid accumulation product (LAP), to evaluate their ability to predict MetS in CKD patients with and without Type 2 diabetes mellitus (T2DM) under various criteria. Multivariate logistic regression analysis was used to investigate the independent associations between the indices and metabolic syndrome among 547 non-dialysis CKD patients, aged ≥18 years. The predictive power of these indices was assessed using receiver operating characteristic (ROC) curve analysis. After adjusting for potential confounders, the correlation between VAI and MetS was strongest based on the optimal cut-off value of 1.51 (sensitivity 86.84%, specificity 91.18%) and 2.35 (sensitivity 83.54%, specificity 86.08%), with OR values of 40.585 (8.683-189.695) and 5.076 (1.247-20.657) for males and females with CKD and T2DM. In CKD patients without T2DM, based on the optimal cut-off values of 1.806 (sensitivity 98.11%, specificity 72.73%) and 3.11 (sensitivity 84.62%, specificity 83.82%), the OR values were 7.514 (3.757-15.027) and 3.008 (1.789-5.056) for males and females, respectively. The area under ROC curve (AUC) and Youden index of VAI were the highest among the seven indexes, indicating its superiority in predicting MetS in both male and female CKD patients, especially those with T2DM.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>35405947</pmid><doi>10.3390/nu14071334</doi><orcidid>https://orcid.org/0000-0002-4184-2699</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adipose tissue Adiposity Adolescent Adult Blood pressure Body mass Body Mass Index Body size Cardiovascular disease China - epidemiology Cholesterol chronic kidney disease Diabetes Diabetes mellitus Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - complications Dialysis Fasting Female Glucose High density lipoprotein Humans Hyperglycemia Hypertension Kidney diseases Kidneys lipid accumulation product Lipids Lipoproteins Male Metabolic disorders Metabolic syndrome Metabolic Syndrome - complications Metabolic Syndrome - diagnosis Mortality Obesity Obesity - complications Obesity, Abdominal - complications Patients Peritoneal dialysis Regression analysis Renal Insufficiency, Chronic - complications Renal Insufficiency, Chronic - diagnosis ROC Curve Sensitivity Statistical analysis type 2 diabetes mellitus visceral adiposity index visceral fat area Waist Circumference |
title | Optimal Obesity- and Lipid-Related Indices for Predicting Metabolic Syndrome in Chronic Kidney Disease Patients with and without Type 2 Diabetes Mellitus in China |
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