Differences in metabolic syndrome severity and prevalence across nine waist circumference measurements collected from smartphone digital anthropometrics
Given the technological advances in 3D smartphone (SP) anthropometry, this technique presents a unique opportunity to improve metabolic syndrome (MetS) screening through optimal waist circumference (WC) landmarking procedures. Thus, the purpose of this study was to evaluate the associations between...
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| Veröffentlicht in: | Clinical nutrition ESPEN 2024-12, Vol.64, p.390-399 |
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| creator | Graybeal, Austin J. Brandner, Caleb F. Compton, Abby T. Swafford, Sydney H. Aultman, Ryan S. Vallecillo-Bustos, Anabelle Stavres, Jon |
| description | Given the technological advances in 3D smartphone (SP) anthropometry, this technique presents a unique opportunity to improve metabolic syndrome (MetS) screening through optimal waist circumference (WC) landmarking procedures. Thus, the purpose of this study was to evaluate the associations between individual MetS risk factors and nine independent WC sites collected using tape measurement or SP anthropometrics and to determine the differences in MetS severity and prevalence when using these different WC measurement locations.
A total of 130 participants (F:74, M:56; age: 27.8 ± 11.1) completed this cross-sectional evaluation. Using traditional tape measurement, WC was measured at the lowest rib (WCRib), superior iliac crest (WCIliac), and between the WCRib and WCIliac (WCMid). Additionally, WC measurements were automated using a SP application at six sites along the torso. MetS risk factors were used to calculate MetS severity (MetSindex) and prevalence. Associations were evaluated using multiple linear regression, the effect of each WC site on MetSindex was analyzed using mixed-models ANCOVA, and differences in MetS prevalence using WCIliac as the current standard were determined using sensitivity, specificity, chi-squared tests, and odds ratios.
The reference SP-WC (SPRef) and WCRib demonstrated the largest associations (all p |
| doi_str_mv | 10.1016/j.clnesp.2024.10.158 |
| format | Article |
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A total of 130 participants (F:74, M:56; age: 27.8 ± 11.1) completed this cross-sectional evaluation. Using traditional tape measurement, WC was measured at the lowest rib (WCRib), superior iliac crest (WCIliac), and between the WCRib and WCIliac (WCMid). Additionally, WC measurements were automated using a SP application at six sites along the torso. MetS risk factors were used to calculate MetS severity (MetSindex) and prevalence. Associations were evaluated using multiple linear regression, the effect of each WC site on MetSindex was analyzed using mixed-models ANCOVA, and differences in MetS prevalence using WCIliac as the current standard were determined using sensitivity, specificity, chi-squared tests, and odds ratios.
The reference SP-WC (SPRef) and WCRib demonstrated the largest associations (all p < 0.001) with HDL cholesterol (SPRef: −0.48; WCRib: −0.49), systolic (SPRef: 0.32; WCRib: 0.30) and diastolic blood pressure (SPRef: 0.34; WCRib: 0.32), and fasting blood glucose (SPRef: 0.38; WCRib: 0.37). SPRef and WCRib were the only WC without significantly different MetSindex; yet demonstrated lower MetSindex and sensitivity (SPRef: 77.8 %; WCRib: 74.1 %) relative to WCIliac, the conventional (or standard) WC measure.
Compared to the current standard, SPRef and WCRib protocols are more highly associated with individual MetS risk factors and produce different MetSindex and diagnoses; highlighting the need for new MetS WC protocols. Given the surge in remote/mobile healthcare, SPRef may be an alternative to traditional methods in this context but requires further investigation before implementation.
[Display omitted]</description><identifier>ISSN: 2405-4577</identifier><identifier>EISSN: 2405-4577</identifier><identifier>DOI: 10.1016/j.clnesp.2024.10.158</identifier><identifier>PMID: 39486478</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Adult ; Anthropometry ; Artificial intelligence ; Body composition ; Cross-Sectional Studies ; Female ; Humans ; Male ; Metabolic syndrome ; Metabolic Syndrome - diagnosis ; Metabolic Syndrome - epidemiology ; Middle Aged ; Mobile applications ; Obesity ; Prevalence ; Risk Factors ; Severity of Illness Index ; Smartphone ; Waist Circumference ; Young Adult</subject><ispartof>Clinical nutrition ESPEN, 2024-12, Vol.64, p.390-399</ispartof><rights>2024 European Society for Clinical Nutrition and Metabolism</rights><rights>Copyright © 2024 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c241t-ab3ddc6d3a6df385f203d32234f568b1c63f29efdd4c9809bc2b5d579780b71f3</cites><orcidid>0000-0003-4520-9230</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39486478$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Graybeal, Austin J.</creatorcontrib><creatorcontrib>Brandner, Caleb F.</creatorcontrib><creatorcontrib>Compton, Abby T.</creatorcontrib><creatorcontrib>Swafford, Sydney H.</creatorcontrib><creatorcontrib>Aultman, Ryan S.</creatorcontrib><creatorcontrib>Vallecillo-Bustos, Anabelle</creatorcontrib><creatorcontrib>Stavres, Jon</creatorcontrib><title>Differences in metabolic syndrome severity and prevalence across nine waist circumference measurements collected from smartphone digital anthropometrics</title><title>Clinical nutrition ESPEN</title><addtitle>Clin Nutr ESPEN</addtitle><description>Given the technological advances in 3D smartphone (SP) anthropometry, this technique presents a unique opportunity to improve metabolic syndrome (MetS) screening through optimal waist circumference (WC) landmarking procedures. Thus, the purpose of this study was to evaluate the associations between individual MetS risk factors and nine independent WC sites collected using tape measurement or SP anthropometrics and to determine the differences in MetS severity and prevalence when using these different WC measurement locations.
A total of 130 participants (F:74, M:56; age: 27.8 ± 11.1) completed this cross-sectional evaluation. Using traditional tape measurement, WC was measured at the lowest rib (WCRib), superior iliac crest (WCIliac), and between the WCRib and WCIliac (WCMid). Additionally, WC measurements were automated using a SP application at six sites along the torso. MetS risk factors were used to calculate MetS severity (MetSindex) and prevalence. Associations were evaluated using multiple linear regression, the effect of each WC site on MetSindex was analyzed using mixed-models ANCOVA, and differences in MetS prevalence using WCIliac as the current standard were determined using sensitivity, specificity, chi-squared tests, and odds ratios.
The reference SP-WC (SPRef) and WCRib demonstrated the largest associations (all p < 0.001) with HDL cholesterol (SPRef: −0.48; WCRib: −0.49), systolic (SPRef: 0.32; WCRib: 0.30) and diastolic blood pressure (SPRef: 0.34; WCRib: 0.32), and fasting blood glucose (SPRef: 0.38; WCRib: 0.37). SPRef and WCRib were the only WC without significantly different MetSindex; yet demonstrated lower MetSindex and sensitivity (SPRef: 77.8 %; WCRib: 74.1 %) relative to WCIliac, the conventional (or standard) WC measure.
Compared to the current standard, SPRef and WCRib protocols are more highly associated with individual MetS risk factors and produce different MetSindex and diagnoses; highlighting the need for new MetS WC protocols. Given the surge in remote/mobile healthcare, SPRef may be an alternative to traditional methods in this context but requires further investigation before implementation.
[Display omitted]</description><subject>Adult</subject><subject>Anthropometry</subject><subject>Artificial intelligence</subject><subject>Body composition</subject><subject>Cross-Sectional Studies</subject><subject>Female</subject><subject>Humans</subject><subject>Male</subject><subject>Metabolic syndrome</subject><subject>Metabolic Syndrome - diagnosis</subject><subject>Metabolic Syndrome - epidemiology</subject><subject>Middle Aged</subject><subject>Mobile applications</subject><subject>Obesity</subject><subject>Prevalence</subject><subject>Risk Factors</subject><subject>Severity of Illness Index</subject><subject>Smartphone</subject><subject>Waist Circumference</subject><subject>Young Adult</subject><issn>2405-4577</issn><issn>2405-4577</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9UcluFDEQtRARiUL-ACEfuczEay8XJBRCEikSF3K23HaZeOR2N7Z70PwJn4s7MyBOnKpUeotePYTeUbKlhDbXu60JEfK8ZYSJ7XqV3St0wQSRGyHb9vU_-zm6ynlHSOX1vaDkDTrnvega0XYX6Ndn7xwkiAYy9hGPUPQwBW9wPkSbphFwhj0kXw5YR4vnBHsdVjjWJk054-gj4J_a54KNT2YZT3JVSuclwQixZGymEMAUsNhVUZxHncr8PFWq9d990aGql-c0zdWxJG_yW3TmdMhwdZqX6OnL7beb-83j17uHm0-PG8MELRs9cGtNY7lurOOddIxwyxnjwsmmG6hpuGM9OGuF6TvSD4YN0sq2bzsytNTxS_ThqDun6ccCuajRZwMh6AjTkhWnjEvRN7SrUHGEvgRP4NScfA1yUJSotRa1U8da1FrLy1WutPcnh2UYwf4l_SmhAj4eAVBz7j0klY1fX2h9qj9TdvL_d_gNkW-loQ</recordid><startdate>202412</startdate><enddate>202412</enddate><creator>Graybeal, Austin J.</creator><creator>Brandner, Caleb F.</creator><creator>Compton, Abby T.</creator><creator>Swafford, Sydney H.</creator><creator>Aultman, Ryan S.</creator><creator>Vallecillo-Bustos, Anabelle</creator><creator>Stavres, Jon</creator><general>Elsevier Ltd</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>7X8</scope><orcidid>https://orcid.org/0000-0003-4520-9230</orcidid></search><sort><creationdate>202412</creationdate><title>Differences in metabolic syndrome severity and prevalence across nine waist circumference measurements collected from smartphone digital anthropometrics</title><author>Graybeal, Austin J. ; Brandner, Caleb F. ; Compton, Abby T. ; Swafford, Sydney H. ; Aultman, Ryan S. ; Vallecillo-Bustos, Anabelle ; Stavres, Jon</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c241t-ab3ddc6d3a6df385f203d32234f568b1c63f29efdd4c9809bc2b5d579780b71f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adult</topic><topic>Anthropometry</topic><topic>Artificial intelligence</topic><topic>Body composition</topic><topic>Cross-Sectional Studies</topic><topic>Female</topic><topic>Humans</topic><topic>Male</topic><topic>Metabolic syndrome</topic><topic>Metabolic Syndrome - diagnosis</topic><topic>Metabolic Syndrome - epidemiology</topic><topic>Middle Aged</topic><topic>Mobile applications</topic><topic>Obesity</topic><topic>Prevalence</topic><topic>Risk Factors</topic><topic>Severity of Illness Index</topic><topic>Smartphone</topic><topic>Waist Circumference</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Graybeal, Austin J.</creatorcontrib><creatorcontrib>Brandner, Caleb F.</creatorcontrib><creatorcontrib>Compton, Abby T.</creatorcontrib><creatorcontrib>Swafford, Sydney H.</creatorcontrib><creatorcontrib>Aultman, Ryan S.</creatorcontrib><creatorcontrib>Vallecillo-Bustos, Anabelle</creatorcontrib><creatorcontrib>Stavres, Jon</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Clinical nutrition ESPEN</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Graybeal, Austin J.</au><au>Brandner, Caleb F.</au><au>Compton, Abby T.</au><au>Swafford, Sydney H.</au><au>Aultman, Ryan S.</au><au>Vallecillo-Bustos, Anabelle</au><au>Stavres, Jon</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Differences in metabolic syndrome severity and prevalence across nine waist circumference measurements collected from smartphone digital anthropometrics</atitle><jtitle>Clinical nutrition ESPEN</jtitle><addtitle>Clin Nutr ESPEN</addtitle><date>2024-12</date><risdate>2024</risdate><volume>64</volume><spage>390</spage><epage>399</epage><pages>390-399</pages><issn>2405-4577</issn><eissn>2405-4577</eissn><abstract>Given the technological advances in 3D smartphone (SP) anthropometry, this technique presents a unique opportunity to improve metabolic syndrome (MetS) screening through optimal waist circumference (WC) landmarking procedures. Thus, the purpose of this study was to evaluate the associations between individual MetS risk factors and nine independent WC sites collected using tape measurement or SP anthropometrics and to determine the differences in MetS severity and prevalence when using these different WC measurement locations.
A total of 130 participants (F:74, M:56; age: 27.8 ± 11.1) completed this cross-sectional evaluation. Using traditional tape measurement, WC was measured at the lowest rib (WCRib), superior iliac crest (WCIliac), and between the WCRib and WCIliac (WCMid). Additionally, WC measurements were automated using a SP application at six sites along the torso. MetS risk factors were used to calculate MetS severity (MetSindex) and prevalence. Associations were evaluated using multiple linear regression, the effect of each WC site on MetSindex was analyzed using mixed-models ANCOVA, and differences in MetS prevalence using WCIliac as the current standard were determined using sensitivity, specificity, chi-squared tests, and odds ratios.
The reference SP-WC (SPRef) and WCRib demonstrated the largest associations (all p < 0.001) with HDL cholesterol (SPRef: −0.48; WCRib: −0.49), systolic (SPRef: 0.32; WCRib: 0.30) and diastolic blood pressure (SPRef: 0.34; WCRib: 0.32), and fasting blood glucose (SPRef: 0.38; WCRib: 0.37). SPRef and WCRib were the only WC without significantly different MetSindex; yet demonstrated lower MetSindex and sensitivity (SPRef: 77.8 %; WCRib: 74.1 %) relative to WCIliac, the conventional (or standard) WC measure.
Compared to the current standard, SPRef and WCRib protocols are more highly associated with individual MetS risk factors and produce different MetSindex and diagnoses; highlighting the need for new MetS WC protocols. Given the surge in remote/mobile healthcare, SPRef may be an alternative to traditional methods in this context but requires further investigation before implementation.
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| ispartof | Clinical nutrition ESPEN, 2024-12, Vol.64, p.390-399 |
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| subjects | Adult Anthropometry Artificial intelligence Body composition Cross-Sectional Studies Female Humans Male Metabolic syndrome Metabolic Syndrome - diagnosis Metabolic Syndrome - epidemiology Middle Aged Mobile applications Obesity Prevalence Risk Factors Severity of Illness Index Smartphone Waist Circumference Young Adult |
| title | Differences in metabolic syndrome severity and prevalence across nine waist circumference measurements collected from smartphone digital anthropometrics |
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