Loading…

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...

Full description

Saved in:
Bibliographic Details
Published in:Nutrients 2022-03, Vol.14 (7), p.1334
Main Authors: Li, Hangtian, Wang, Qian, Ke, Jianghua, Lin, Wenwen, Luo, Yayong, Yao, Jin, Zhang, Weiguang, Zhang, Li, Duan, Shuwei, Dong, Zheyi, Chen, Xiangmei
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c472t-20b393af10614740ca75a79b63587a8f50c942fe2208a42ff3178eeec335a3623
cites cdi_FETCH-LOGICAL-c472t-20b393af10614740ca75a79b63587a8f50c942fe2208a42ff3178eeec335a3623
container_end_page
container_issue 7
container_start_page 1334
container_title Nutrients
container_volume 14
creator Li, Hangtian
Wang, Qian
Ke, Jianghua
Lin, Wenwen
Luo, Yayong
Yao, Jin
Zhang, Weiguang
Zhang, Li
Duan, Shuwei
Dong, Zheyi
Chen, Xiangmei
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.
doi_str_mv 10.3390/nu14071334
format article
fullrecord <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_4dfbb43eb8544f668b380805a7708800</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_4dfbb43eb8544f668b380805a7708800</doaj_id><sourcerecordid>2649592590</sourcerecordid><originalsourceid>FETCH-LOGICAL-c472t-20b393af10614740ca75a79b63587a8f50c942fe2208a42ff3178eeec335a3623</originalsourceid><addsrcrecordid>eNpdks9u1DAQxiMEotXSCw-ALHFBSIGJ7cTJBQkt_1ZstRWUs-Ukk12vsnZqO6C8Dk-Ks1tKiy8ej3_6vvF4kuR5Bm8Yq-CtGTMOImOMP0rOKQiaFgVnj-_FZ8mF93uYlwBRsKfJGcs55BUX58nvzRD0QfVkU6PXYUqJMi1Z60G36TfsVcCWrEyrG_Sks45cOYyHoM2WXGJQte11Q75PpnX2gEQbstw5a2Luq24NTuSD9qg8kisVNJrgyS8ddkePObBjINfTgIRGUNUYossl9r0Ooz-JaaOeJU861Xu8uN0XyY9PH6-XX9L15vNq-X6dNlzQkFKoWcVUl0GRccGhUSJXoqoLlpdClV0OTcVph5RCqWLQsUyUiNgwlitWULZIVifd1qq9HFxsi5ukVVoeE9ZtpXJBNz1K3nZ1zRnWZc55VxRlzUooIfoJKEuAqPXupDWM9QHbJj7dqf6B6MMbo3dya3_KCoCy-GmL5NWtgLM3I_ogD9o3sTXKoB29pAWv8orm1ez18j90b0dnYquOFMylVZF6faIaZ7132N0Vk4GcJ0n-m6QIv7hf_h36d27YHzjpw3s</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2649038089</pqid></control><display><type>article</type><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><source>Publicly Available Content Database</source><source>PubMed Central</source><creator>Li, Hangtian ; Wang, Qian ; Ke, Jianghua ; Lin, Wenwen ; Luo, Yayong ; Yao, Jin ; Zhang, Weiguang ; Zhang, Li ; Duan, Shuwei ; Dong, Zheyi ; Chen, Xiangmei</creator><creatorcontrib>Li, Hangtian ; Wang, Qian ; Ke, Jianghua ; Lin, Wenwen ; Luo, Yayong ; Yao, Jin ; Zhang, Weiguang ; Zhang, Li ; Duan, Shuwei ; Dong, Zheyi ; Chen, Xiangmei</creatorcontrib><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><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 &amp; 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 &amp; Medical Complete (Alumni)</collection><collection>Health &amp; 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>
fulltext fulltext
identifier ISSN: 2072-6643
ispartof Nutrients, 2022-03, Vol.14 (7), p.1334
issn 2072-6643
2072-6643
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_4dfbb43eb8544f668b380805a7708800
source Publicly Available Content Database; PubMed Central
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
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T18%3A34%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Optimal%20Obesity-%20and%20Lipid-Related%20Indices%20for%20Predicting%20Metabolic%20Syndrome%20in%20Chronic%20Kidney%20Disease%20Patients%20with%20and%20without%20Type%202%20Diabetes%20Mellitus%20in%20China&rft.jtitle=Nutrients&rft.au=Li,%20Hangtian&rft.date=2022-03-23&rft.volume=14&rft.issue=7&rft.spage=1334&rft.pages=1334-&rft.issn=2072-6643&rft.eissn=2072-6643&rft_id=info:doi/10.3390/nu14071334&rft_dat=%3Cproquest_doaj_%3E2649592590%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c472t-20b393af10614740ca75a79b63587a8f50c942fe2208a42ff3178eeec335a3623%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2649038089&rft_id=info:pmid/35405947&rfr_iscdi=true