Dysregulation of Circulating miR-24-3p in Children with Obesity and Its Predictive Value for Metabolic Syndrome
Introduction: Obesity is a major risk factor for metabolic disorders in children. Therefore, it is particularly important to study the abnormal regulation of circulating miR-24-3p in obese children and its predictive value for metabolic syndrome. Methods: Serum samples were obtained from children wi...
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description | Introduction: Obesity is a major risk factor for metabolic disorders in children. Therefore, it is particularly important to study the abnormal regulation of circulating miR-24-3p in obese children and its predictive value for metabolic syndrome. Methods: Serum samples were obtained from children with obesity (n = 45), obese children with metabolic syndrome (n = 52), and healthy controls (n = 50). The expression levels of miR-24-3p were detected by reverse transcription quantitative PCR. The ROC curve was used to evaluate the diagnostic value of miR-24-3p. Pearson’s correlation analysis was performed to evaluate the relationship between serum miR-24-3p and different clinical parameters. Logistic regression analysis was used to evaluate the relationship between miR-24-3p and obesity with metabolic syndrome in children. Results: The expression of miR-24-3p was the highest in obese children with metabolic syndrome. ROC results showed that miR-24-3p had the ability to distinguish healthy individuals from obese children (area under the curve [AUC] = 0.951) and can predict the occurrence of metabolic syndrome for obese children (AUC = 0.890). The expression level of miR-24-3p was positively correlated with body mass index (r = 0.817, p < 0.001), fasting blood glucose (r = 0.798, p < 0.001), triglycerides (r = 0.773, p < 0.001), systolic blood pressure (r = 0.746, p < 0.001), and diastolic blood pressure (r = 0.623, p < 0.001), respectively. Logistic regression analysis showed that miR-24-3p was an independent influence factor for the occurrence of metabolic syndrome in obese children. Discussion/Conclusion: MiR-24-3p is a potential noninvasive marker for children with obesity and has predictive value for the occurrence of metabolic syndrome. |
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Therefore, it is particularly important to study the abnormal regulation of circulating miR-24-3p in obese children and its predictive value for metabolic syndrome. Methods: Serum samples were obtained from children with obesity (n = 45), obese children with metabolic syndrome (n = 52), and healthy controls (n = 50). The expression levels of miR-24-3p were detected by reverse transcription quantitative PCR. The ROC curve was used to evaluate the diagnostic value of miR-24-3p. Pearson’s correlation analysis was performed to evaluate the relationship between serum miR-24-3p and different clinical parameters. Logistic regression analysis was used to evaluate the relationship between miR-24-3p and obesity with metabolic syndrome in children. Results: The expression of miR-24-3p was the highest in obese children with metabolic syndrome. ROC results showed that miR-24-3p had the ability to distinguish healthy individuals from obese children (area under the curve [AUC] = 0.951) and can predict the occurrence of metabolic syndrome for obese children (AUC = 0.890). The expression level of miR-24-3p was positively correlated with body mass index (r = 0.817, p < 0.001), fasting blood glucose (r = 0.798, p < 0.001), triglycerides (r = 0.773, p < 0.001), systolic blood pressure (r = 0.746, p < 0.001), and diastolic blood pressure (r = 0.623, p < 0.001), respectively. Logistic regression analysis showed that miR-24-3p was an independent influence factor for the occurrence of metabolic syndrome in obese children. Discussion/Conclusion: MiR-24-3p is a potential noninvasive marker for children with obesity and has predictive value for the occurrence of metabolic syndrome.</description><identifier>ISSN: 1662-4025</identifier><identifier>EISSN: 1662-4033</identifier><identifier>DOI: 10.1159/000515720</identifier><identifier>PMID: 34428771</identifier><language>eng</language><publisher>Basel, Switzerland: S. Karger AG</publisher><subject>Biomarkers ; Blood pressure ; Body mass index ; Child ; Cholesterol ; Complications and side effects ; Correlation analysis ; Demographic aspects ; Diabetes ; Diagnosis ; Females ; Gene expression ; Guardians ; Health aspects ; Health care ; High density lipoprotein ; Humans ; Metabolic diseases ; Metabolic syndrome ; Metabolic Syndrome - complications ; Metabolic Syndrome - diagnosis ; Metabolic Syndrome - genetics ; MicroRNA ; MicroRNAs ; MicroRNAs - genetics ; mir-24-3p ; Obesity ; Obesity in children ; Pediatric Obesity - complications ; Pediatric Obesity - diagnosis ; Pediatric Obesity - genetics ; Research Article ; Risk factors ; ROC Curve ; Sample size ; Software ; Value analysis</subject><ispartof>Obesity Facts, 2021-10, Vol.14 (5), p.456-462</ispartof><rights>2021 The Author(s) Published by S. Karger AG, Basel</rights><rights>2021 The Author(s) Published by S. Karger AG, Basel.</rights><rights>COPYRIGHT 2021 S. Karger AG</rights><rights>2021 The Author(s) Published by S. Karger AG, Basel . This work is licensed under the Creative Commons Attribution – Non-Commercial License http://creativecommons.org/licenses/by-nc/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Copyright © 2021 by S. Karger AG, Basel 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c585t-d390540eda88c7f223753a08d02c57a01f7df2a81b5b39669df03650bd5ccef53</citedby><cites>FETCH-LOGICAL-c585t-d390540eda88c7f223753a08d02c57a01f7df2a81b5b39669df03650bd5ccef53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8546450/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8546450/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,27612,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34428771$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Bingjin</creatorcontrib><creatorcontrib>Xing, Lingling</creatorcontrib><creatorcontrib>Wang, Beibei</creatorcontrib><title>Dysregulation of Circulating miR-24-3p in Children with Obesity and Its Predictive Value for Metabolic Syndrome</title><title>Obesity Facts</title><addtitle>Obes Facts</addtitle><description>Introduction: Obesity is a major risk factor for metabolic disorders in children. Therefore, it is particularly important to study the abnormal regulation of circulating miR-24-3p in obese children and its predictive value for metabolic syndrome. Methods: Serum samples were obtained from children with obesity (n = 45), obese children with metabolic syndrome (n = 52), and healthy controls (n = 50). The expression levels of miR-24-3p were detected by reverse transcription quantitative PCR. The ROC curve was used to evaluate the diagnostic value of miR-24-3p. Pearson’s correlation analysis was performed to evaluate the relationship between serum miR-24-3p and different clinical parameters. Logistic regression analysis was used to evaluate the relationship between miR-24-3p and obesity with metabolic syndrome in children. Results: The expression of miR-24-3p was the highest in obese children with metabolic syndrome. ROC results showed that miR-24-3p had the ability to distinguish healthy individuals from obese children (area under the curve [AUC] = 0.951) and can predict the occurrence of metabolic syndrome for obese children (AUC = 0.890). The expression level of miR-24-3p was positively correlated with body mass index (r = 0.817, p < 0.001), fasting blood glucose (r = 0.798, p < 0.001), triglycerides (r = 0.773, p < 0.001), systolic blood pressure (r = 0.746, p < 0.001), and diastolic blood pressure (r = 0.623, p < 0.001), respectively. Logistic regression analysis showed that miR-24-3p was an independent influence factor for the occurrence of metabolic syndrome in obese children. Discussion/Conclusion: MiR-24-3p is a potential noninvasive marker for children with obesity and has predictive value for the occurrence of metabolic syndrome.</description><subject>Biomarkers</subject><subject>Blood pressure</subject><subject>Body mass index</subject><subject>Child</subject><subject>Cholesterol</subject><subject>Complications and side effects</subject><subject>Correlation analysis</subject><subject>Demographic aspects</subject><subject>Diabetes</subject><subject>Diagnosis</subject><subject>Females</subject><subject>Gene expression</subject><subject>Guardians</subject><subject>Health aspects</subject><subject>Health care</subject><subject>High density lipoprotein</subject><subject>Humans</subject><subject>Metabolic diseases</subject><subject>Metabolic syndrome</subject><subject>Metabolic Syndrome - complications</subject><subject>Metabolic Syndrome - diagnosis</subject><subject>Metabolic Syndrome - genetics</subject><subject>MicroRNA</subject><subject>MicroRNAs</subject><subject>MicroRNAs - genetics</subject><subject>mir-24-3p</subject><subject>Obesity</subject><subject>Obesity in children</subject><subject>Pediatric Obesity - complications</subject><subject>Pediatric Obesity - diagnosis</subject><subject>Pediatric Obesity - genetics</subject><subject>Research Article</subject><subject>Risk factors</subject><subject>ROC Curve</subject><subject>Sample size</subject><subject>Software</subject><subject>Value analysis</subject><issn>1662-4025</issn><issn>1662-4033</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>M--</sourceid><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><sourceid>DOA</sourceid><recordid>eNptkstv1DAQhyMEoqVw4I6QpV7gkOK3k0ul1UJhpaIiXlfL8SPrJbEXJyna_x63KRFFyAd7Zr752TOeoniO4BlCrH4DIWSICQwfFMeIc1xSSMjD5YzZUfFkGHYQckQFelwcEUpxJQQ6LuLbw5BsO3Vq9DGA6MDaJ31rhhb0_nOJaUn2wAew3vrOJBvALz9uwVVjBz8egAoGbMYBfErWeD36awu-q26ywMUEPtpRNbHzGnw5BJNib58Wj5zqBvvsbj8pvl28-7r-UF5evd-sV5elZhUbS0NqyCi0RlWVFg5jIhhRsDIQayYURE4Yh1WFGtaQmvPaOEg4g41hWlvHyEmxmXVNVDu5T75X6SCj8vLWEVMrVRq97qx0tkLQ4toZJqhjpjGV0RYyrVClHXVZ63zW2k9Nb3MsjEl190TvR4LfyjZey4pRThnMAq_uBFL8OdlhlL0ftO06FWycBokZp7SmGNGMnv6D7uKUQm6VxDWEBHNe8UydzVSrcgE-uJjv1XkZ23sdg3U--1cCMUiFEDfteD0n6BSH_OFueT2C8maG5DJDmX35d7kL-WdoMvBiBn6o1Nq0AEv-6X_DVxermZB748hvOv7WEQ</recordid><startdate>20211001</startdate><enddate>20211001</enddate><creator>Zhang, Bingjin</creator><creator>Xing, Lingling</creator><creator>Wang, Beibei</creator><general>S. Karger AG</general><general>Karger Publishers</general><scope>M--</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>IAO</scope><scope>3V.</scope><scope>7WY</scope><scope>7X7</scope><scope>7XB</scope><scope>883</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>M0F</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PADUT</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYYUZ</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20211001</creationdate><title>Dysregulation of Circulating miR-24-3p in Children with Obesity and Its Predictive Value for Metabolic Syndrome</title><author>Zhang, Bingjin ; Xing, Lingling ; Wang, Beibei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c585t-d390540eda88c7f223753a08d02c57a01f7df2a81b5b39669df03650bd5ccef53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Biomarkers</topic><topic>Blood pressure</topic><topic>Body mass index</topic><topic>Child</topic><topic>Cholesterol</topic><topic>Complications and side effects</topic><topic>Correlation analysis</topic><topic>Demographic aspects</topic><topic>Diabetes</topic><topic>Diagnosis</topic><topic>Females</topic><topic>Gene expression</topic><topic>Guardians</topic><topic>Health aspects</topic><topic>Health care</topic><topic>High density lipoprotein</topic><topic>Humans</topic><topic>Metabolic diseases</topic><topic>Metabolic syndrome</topic><topic>Metabolic Syndrome - complications</topic><topic>Metabolic Syndrome - diagnosis</topic><topic>Metabolic Syndrome - genetics</topic><topic>MicroRNA</topic><topic>MicroRNAs</topic><topic>MicroRNAs - genetics</topic><topic>mir-24-3p</topic><topic>Obesity</topic><topic>Obesity in children</topic><topic>Pediatric Obesity - complications</topic><topic>Pediatric Obesity - diagnosis</topic><topic>Pediatric Obesity - genetics</topic><topic>Research Article</topic><topic>Risk factors</topic><topic>ROC Curve</topic><topic>Sample size</topic><topic>Software</topic><topic>Value analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Bingjin</creatorcontrib><creatorcontrib>Xing, Lingling</creatorcontrib><creatorcontrib>Wang, Beibei</creatorcontrib><collection>Karger 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>Gale Academic OneFile</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Trade & Industry (Alumni Edition)</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>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</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>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ABI/INFORM Trade & Industry</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Research Library China</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Obesity Facts</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Bingjin</au><au>Xing, Lingling</au><au>Wang, Beibei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dysregulation of Circulating miR-24-3p in Children with Obesity and Its Predictive Value for Metabolic Syndrome</atitle><jtitle>Obesity Facts</jtitle><addtitle>Obes Facts</addtitle><date>2021-10-01</date><risdate>2021</risdate><volume>14</volume><issue>5</issue><spage>456</spage><epage>462</epage><pages>456-462</pages><issn>1662-4025</issn><eissn>1662-4033</eissn><abstract>Introduction: Obesity is a major risk factor for metabolic disorders in children. Therefore, it is particularly important to study the abnormal regulation of circulating miR-24-3p in obese children and its predictive value for metabolic syndrome. Methods: Serum samples were obtained from children with obesity (n = 45), obese children with metabolic syndrome (n = 52), and healthy controls (n = 50). The expression levels of miR-24-3p were detected by reverse transcription quantitative PCR. The ROC curve was used to evaluate the diagnostic value of miR-24-3p. Pearson’s correlation analysis was performed to evaluate the relationship between serum miR-24-3p and different clinical parameters. Logistic regression analysis was used to evaluate the relationship between miR-24-3p and obesity with metabolic syndrome in children. Results: The expression of miR-24-3p was the highest in obese children with metabolic syndrome. ROC results showed that miR-24-3p had the ability to distinguish healthy individuals from obese children (area under the curve [AUC] = 0.951) and can predict the occurrence of metabolic syndrome for obese children (AUC = 0.890). The expression level of miR-24-3p was positively correlated with body mass index (r = 0.817, p < 0.001), fasting blood glucose (r = 0.798, p < 0.001), triglycerides (r = 0.773, p < 0.001), systolic blood pressure (r = 0.746, p < 0.001), and diastolic blood pressure (r = 0.623, p < 0.001), respectively. Logistic regression analysis showed that miR-24-3p was an independent influence factor for the occurrence of metabolic syndrome in obese children. Discussion/Conclusion: MiR-24-3p is a potential noninvasive marker for children with obesity and has predictive value for the occurrence of metabolic syndrome.</abstract><cop>Basel, Switzerland</cop><pub>S. Karger AG</pub><pmid>34428771</pmid><doi>10.1159/000515720</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Biomarkers Blood pressure Body mass index Child Cholesterol Complications and side effects Correlation analysis Demographic aspects Diabetes Diagnosis Females Gene expression Guardians Health aspects Health care High density lipoprotein Humans Metabolic diseases Metabolic syndrome Metabolic Syndrome - complications Metabolic Syndrome - diagnosis Metabolic Syndrome - genetics MicroRNA MicroRNAs MicroRNAs - genetics mir-24-3p Obesity Obesity in children Pediatric Obesity - complications Pediatric Obesity - diagnosis Pediatric Obesity - genetics Research Article Risk factors ROC Curve Sample size Software Value analysis |
title | Dysregulation of Circulating miR-24-3p in Children with Obesity and Its Predictive Value for Metabolic Syndrome |
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