Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the Shape Up! studies
Three-dimensional optical (3DO) body scanning has been proposed for automatic anthropometry. However, conventional measurements fail to capture detailed body shape. More sophisticated shape features could better indicate health status. The objectives were to predict DXA total and regional body compo...
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| Veröffentlicht in: | The American journal of clinical nutrition 2019-12, Vol.110 (6), p.1316-1326 |
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| creator | Ng, Bennett K Sommer, Markus J Wong, Michael C Pagano, Ian Nie, Yilin Fan, Bo Kennedy, Samantha Bourgeois, Brianna Kelly, Nisa Liu, Yong E Hwaung, Phoenix Garber, Andrea K Chow, Dominic Vaisse, Christian Curless, Brian Heymsfield, Steven B Shepherd, John A |
| description | Three-dimensional optical (3DO) body scanning has been proposed for automatic anthropometry. However, conventional measurements fail to capture detailed body shape. More sophisticated shape features could better indicate health status.
The objectives were to predict DXA total and regional body composition, serum lipid and diabetes markers, and functional strength from 3DO body scans using statistical shape modeling.
Healthy adults underwent whole-body 3DO and DXA scans, blood tests, and strength assessments in the Shape Up! Adults cross-sectional observational study. Principal component analysis was performed on registered 3DO scans. Stepwise linear regressions were performed to estimate body composition, serum biomarkers, and strength using 3DO principal components (PCs). 3DO model accuracy was compared with simple anthropometric models and precision was compared with DXA.
This analysis included 407 subjects. Eleven PCs for each sex captured 95% of body shape variance. 3DO body composition accuracy to DXA was: fat mass R2 = 0.88 male, 0.93 female; visceral fat mass R2 = 0.67 male, 0.75 female. 3DO body fat test-retest precision was: root mean squared error = 0.81 kg male, 0.66 kg female. 3DO visceral fat was as precise (%CV = 7.4 for males, 6.8 for females) as DXA (%CV = 6.8 for males, 7.4 for females). Multiple 3DO PCs were significantly correlated with serum HDL cholesterol, triglycerides, glucose, insulin, and HOMA-IR, independent of simple anthropometrics. 3DO PCs improved prediction of isometric knee strength (combined model R2 = 0.67 male, 0.59 female; anthropometrics-only model R2 = 0.34 male, 0.24 female).
3DO body shape PCs predict body composition with good accuracy and precision comparable to existing methods. 3DO PCs improve prediction of serum lipid and diabetes markers, and functional strength measurements. The safety and accessibility of 3DO scanning make it appropriate for monitoring individual body composition, and metabolic health and functional strength in epidemiological settings.
This trial was registered at clinicaltrials.gov as NCT03637855. |
| doi_str_mv | 10.1093/ajcn/nqz218 |
| format | Article |
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The objectives were to predict DXA total and regional body composition, serum lipid and diabetes markers, and functional strength from 3DO body scans using statistical shape modeling.
Healthy adults underwent whole-body 3DO and DXA scans, blood tests, and strength assessments in the Shape Up! Adults cross-sectional observational study. Principal component analysis was performed on registered 3DO scans. Stepwise linear regressions were performed to estimate body composition, serum biomarkers, and strength using 3DO principal components (PCs). 3DO model accuracy was compared with simple anthropometric models and precision was compared with DXA.
This analysis included 407 subjects. Eleven PCs for each sex captured 95% of body shape variance. 3DO body composition accuracy to DXA was: fat mass R2 = 0.88 male, 0.93 female; visceral fat mass R2 = 0.67 male, 0.75 female. 3DO body fat test-retest precision was: root mean squared error = 0.81 kg male, 0.66 kg female. 3DO visceral fat was as precise (%CV = 7.4 for males, 6.8 for females) as DXA (%CV = 6.8 for males, 7.4 for females). Multiple 3DO PCs were significantly correlated with serum HDL cholesterol, triglycerides, glucose, insulin, and HOMA-IR, independent of simple anthropometrics. 3DO PCs improved prediction of isometric knee strength (combined model R2 = 0.67 male, 0.59 female; anthropometrics-only model R2 = 0.34 male, 0.24 female).
3DO body shape PCs predict body composition with good accuracy and precision comparable to existing methods. 3DO PCs improve prediction of serum lipid and diabetes markers, and functional strength measurements. The safety and accessibility of 3DO scanning make it appropriate for monitoring individual body composition, and metabolic health and functional strength in epidemiological settings.
This trial was registered at clinicaltrials.gov as NCT03637855.</description><identifier>ISSN: 0002-9165</identifier><identifier>EISSN: 1938-3207</identifier><identifier>DOI: 10.1093/ajcn/nqz218</identifier><identifier>PMID: 31553429</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Absorptiometry, Photon ; Accuracy ; Adipose Tissue - diagnostic imaging ; Adolescent ; Adult ; Adults ; Anthropometry ; Biomarkers ; Blood ; Body Composition ; Body fat ; Cholesterol ; Cross-Sectional Studies ; Diabetes ; Diabetes mellitus ; Dual energy X-ray absorptiometry ; Editor's Choice ; Epidemiology ; Female ; Females ; High density lipoprotein ; Humans ; imaging ; Imaging, Three-Dimensional ; Insulin ; Insulin - blood ; Isometric ; Knee ; Knee - physiology ; Lipids ; Lipoproteins, HDL - blood ; Male ; Males ; Mathematical models ; Metabolites ; Middle Aged ; Model accuracy ; obesity ; Original Research Communications ; principal component analysis ; Principal components analysis ; Regression analysis ; Scanning ; Statistical methods ; Strength ; Three dimensional bodies ; Triglycerides ; Triglycerides - blood ; Young Adult</subject><ispartof>The American journal of clinical nutrition, 2019-12, Vol.110 (6), p.1316-1326</ispartof><rights>2019 American Society for Nutrition.</rights><rights>Copyright © American Society for Nutrition 2019. 2019</rights><rights>Copyright © American Society for Nutrition 2019.</rights><rights>Copyright American Society for Clinical Nutrition, Inc. Dec 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c486t-c2c5e7eb89baf4a79f521f700812ebd2968ccef84687eb9ed8e68ff0bba423ad3</citedby><cites>FETCH-LOGICAL-c486t-c2c5e7eb89baf4a79f521f700812ebd2968ccef84687eb9ed8e68ff0bba423ad3</cites><orcidid>0000-0003-4625-3161</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31553429$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ng, Bennett K</creatorcontrib><creatorcontrib>Sommer, Markus J</creatorcontrib><creatorcontrib>Wong, Michael C</creatorcontrib><creatorcontrib>Pagano, Ian</creatorcontrib><creatorcontrib>Nie, Yilin</creatorcontrib><creatorcontrib>Fan, Bo</creatorcontrib><creatorcontrib>Kennedy, Samantha</creatorcontrib><creatorcontrib>Bourgeois, Brianna</creatorcontrib><creatorcontrib>Kelly, Nisa</creatorcontrib><creatorcontrib>Liu, Yong E</creatorcontrib><creatorcontrib>Hwaung, Phoenix</creatorcontrib><creatorcontrib>Garber, Andrea K</creatorcontrib><creatorcontrib>Chow, Dominic</creatorcontrib><creatorcontrib>Vaisse, Christian</creatorcontrib><creatorcontrib>Curless, Brian</creatorcontrib><creatorcontrib>Heymsfield, Steven B</creatorcontrib><creatorcontrib>Shepherd, John A</creatorcontrib><title>Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the Shape Up! studies</title><title>The American journal of clinical nutrition</title><addtitle>Am J Clin Nutr</addtitle><description>Three-dimensional optical (3DO) body scanning has been proposed for automatic anthropometry. However, conventional measurements fail to capture detailed body shape. More sophisticated shape features could better indicate health status.
The objectives were to predict DXA total and regional body composition, serum lipid and diabetes markers, and functional strength from 3DO body scans using statistical shape modeling.
Healthy adults underwent whole-body 3DO and DXA scans, blood tests, and strength assessments in the Shape Up! Adults cross-sectional observational study. Principal component analysis was performed on registered 3DO scans. Stepwise linear regressions were performed to estimate body composition, serum biomarkers, and strength using 3DO principal components (PCs). 3DO model accuracy was compared with simple anthropometric models and precision was compared with DXA.
This analysis included 407 subjects. Eleven PCs for each sex captured 95% of body shape variance. 3DO body composition accuracy to DXA was: fat mass R2 = 0.88 male, 0.93 female; visceral fat mass R2 = 0.67 male, 0.75 female. 3DO body fat test-retest precision was: root mean squared error = 0.81 kg male, 0.66 kg female. 3DO visceral fat was as precise (%CV = 7.4 for males, 6.8 for females) as DXA (%CV = 6.8 for males, 7.4 for females). Multiple 3DO PCs were significantly correlated with serum HDL cholesterol, triglycerides, glucose, insulin, and HOMA-IR, independent of simple anthropometrics. 3DO PCs improved prediction of isometric knee strength (combined model R2 = 0.67 male, 0.59 female; anthropometrics-only model R2 = 0.34 male, 0.24 female).
3DO body shape PCs predict body composition with good accuracy and precision comparable to existing methods. 3DO PCs improve prediction of serum lipid and diabetes markers, and functional strength measurements. The safety and accessibility of 3DO scanning make it appropriate for monitoring individual body composition, and metabolic health and functional strength in epidemiological settings.
This trial was registered at clinicaltrials.gov as NCT03637855.</description><subject>Absorptiometry, Photon</subject><subject>Accuracy</subject><subject>Adipose Tissue - diagnostic imaging</subject><subject>Adolescent</subject><subject>Adult</subject><subject>Adults</subject><subject>Anthropometry</subject><subject>Biomarkers</subject><subject>Blood</subject><subject>Body Composition</subject><subject>Body fat</subject><subject>Cholesterol</subject><subject>Cross-Sectional Studies</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Dual energy X-ray absorptiometry</subject><subject>Editor's Choice</subject><subject>Epidemiology</subject><subject>Female</subject><subject>Females</subject><subject>High density lipoprotein</subject><subject>Humans</subject><subject>imaging</subject><subject>Imaging, Three-Dimensional</subject><subject>Insulin</subject><subject>Insulin - blood</subject><subject>Isometric</subject><subject>Knee</subject><subject>Knee - physiology</subject><subject>Lipids</subject><subject>Lipoproteins, HDL - blood</subject><subject>Male</subject><subject>Males</subject><subject>Mathematical models</subject><subject>Metabolites</subject><subject>Middle Aged</subject><subject>Model accuracy</subject><subject>obesity</subject><subject>Original Research Communications</subject><subject>principal component analysis</subject><subject>Principal components analysis</subject><subject>Regression analysis</subject><subject>Scanning</subject><subject>Statistical methods</subject><subject>Strength</subject><subject>Three dimensional bodies</subject><subject>Triglycerides</subject><subject>Triglycerides - blood</subject><subject>Young Adult</subject><issn>0002-9165</issn><issn>1938-3207</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU1rFTEUhoMo9lpduZdIQQQ7Nsl8JS4EqZ9QcKFdh0xy0pvLTDJNMoX2B_R3mzq1qAiuAjnPeXg5L0JPKXlNiaiP1E77I39-xSi_hzZU1LyqGenvow0hhFWCdu0eepTSjhDKGt49RHs1bdu6YWKDrt9DVm4Eg-vKuAl8csGrEQ_BXOK0VTNgCyovERKeIxin8zrTYZpDcrngh3gYQzB4KqohjC5DOsTKG2wXr_PqSzmCP8vbNzhvAX_7KT6dn5f_xThIj9EDq8YET27ffXT68cP348_VyddPX47fnVS6BM-VZrqFHgYuBmUb1QvbMmp7QjhlMBgmOq41WN50vFACDIeOW0uGQTWsVqbeR29X77wMExgNPkc1yjm6ScVLGZSTf06828qzcCE7ztumb4vg5a0ghvMFUpaTSxrGUXkIS5KMiZ4y3tVdQQ_-QndhieUYhaoZ46LnTV-oVyulY0gpgr0LQ4m86Vfe9CvXfgv97Pf8d-yvQgvwYgXCMv_H1K4glHNfOIgyaQdel4oj6CxNcP_c-wES6cYq</recordid><startdate>20191201</startdate><enddate>20191201</enddate><creator>Ng, Bennett K</creator><creator>Sommer, Markus J</creator><creator>Wong, Michael C</creator><creator>Pagano, Ian</creator><creator>Nie, Yilin</creator><creator>Fan, Bo</creator><creator>Kennedy, Samantha</creator><creator>Bourgeois, Brianna</creator><creator>Kelly, Nisa</creator><creator>Liu, Yong E</creator><creator>Hwaung, Phoenix</creator><creator>Garber, Andrea K</creator><creator>Chow, Dominic</creator><creator>Vaisse, Christian</creator><creator>Curless, Brian</creator><creator>Heymsfield, Steven B</creator><creator>Shepherd, John A</creator><general>Elsevier Inc</general><general>Oxford University Press</general><general>American Society for Clinical Nutrition, Inc</general><scope>6I.</scope><scope>AAFTH</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>7QP</scope><scope>7T7</scope><scope>7TS</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-4625-3161</orcidid></search><sort><creationdate>20191201</creationdate><title>Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the Shape Up! studies</title><author>Ng, Bennett K ; Sommer, Markus J ; Wong, Michael C ; Pagano, Ian ; Nie, Yilin ; Fan, Bo ; Kennedy, Samantha ; Bourgeois, Brianna ; Kelly, Nisa ; Liu, Yong E ; Hwaung, Phoenix ; Garber, Andrea K ; Chow, Dominic ; Vaisse, Christian ; Curless, Brian ; Heymsfield, Steven B ; Shepherd, John A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c486t-c2c5e7eb89baf4a79f521f700812ebd2968ccef84687eb9ed8e68ff0bba423ad3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Absorptiometry, Photon</topic><topic>Accuracy</topic><topic>Adipose Tissue - diagnostic imaging</topic><topic>Adolescent</topic><topic>Adult</topic><topic>Adults</topic><topic>Anthropometry</topic><topic>Biomarkers</topic><topic>Blood</topic><topic>Body Composition</topic><topic>Body fat</topic><topic>Cholesterol</topic><topic>Cross-Sectional Studies</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Dual energy X-ray absorptiometry</topic><topic>Editor's Choice</topic><topic>Epidemiology</topic><topic>Female</topic><topic>Females</topic><topic>High density lipoprotein</topic><topic>Humans</topic><topic>imaging</topic><topic>Imaging, Three-Dimensional</topic><topic>Insulin</topic><topic>Insulin - blood</topic><topic>Isometric</topic><topic>Knee</topic><topic>Knee - physiology</topic><topic>Lipids</topic><topic>Lipoproteins, HDL - blood</topic><topic>Male</topic><topic>Males</topic><topic>Mathematical models</topic><topic>Metabolites</topic><topic>Middle Aged</topic><topic>Model accuracy</topic><topic>obesity</topic><topic>Original Research Communications</topic><topic>principal component analysis</topic><topic>Principal components analysis</topic><topic>Regression analysis</topic><topic>Scanning</topic><topic>Statistical methods</topic><topic>Strength</topic><topic>Three dimensional bodies</topic><topic>Triglycerides</topic><topic>Triglycerides - blood</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ng, Bennett K</creatorcontrib><creatorcontrib>Sommer, Markus J</creatorcontrib><creatorcontrib>Wong, Michael C</creatorcontrib><creatorcontrib>Pagano, Ian</creatorcontrib><creatorcontrib>Nie, Yilin</creatorcontrib><creatorcontrib>Fan, Bo</creatorcontrib><creatorcontrib>Kennedy, Samantha</creatorcontrib><creatorcontrib>Bourgeois, Brianna</creatorcontrib><creatorcontrib>Kelly, Nisa</creatorcontrib><creatorcontrib>Liu, Yong E</creatorcontrib><creatorcontrib>Hwaung, Phoenix</creatorcontrib><creatorcontrib>Garber, Andrea K</creatorcontrib><creatorcontrib>Chow, Dominic</creatorcontrib><creatorcontrib>Vaisse, Christian</creatorcontrib><creatorcontrib>Curless, Brian</creatorcontrib><creatorcontrib>Heymsfield, Steven B</creatorcontrib><creatorcontrib>Shepherd, John A</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect: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>Calcium & Calcified Tissue Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Physical Education Index</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>The American journal of clinical nutrition</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ng, Bennett K</au><au>Sommer, Markus J</au><au>Wong, Michael C</au><au>Pagano, Ian</au><au>Nie, Yilin</au><au>Fan, Bo</au><au>Kennedy, Samantha</au><au>Bourgeois, Brianna</au><au>Kelly, Nisa</au><au>Liu, Yong E</au><au>Hwaung, Phoenix</au><au>Garber, Andrea K</au><au>Chow, Dominic</au><au>Vaisse, Christian</au><au>Curless, Brian</au><au>Heymsfield, Steven B</au><au>Shepherd, John A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the Shape Up! studies</atitle><jtitle>The American journal of clinical nutrition</jtitle><addtitle>Am J Clin Nutr</addtitle><date>2019-12-01</date><risdate>2019</risdate><volume>110</volume><issue>6</issue><spage>1316</spage><epage>1326</epage><pages>1316-1326</pages><issn>0002-9165</issn><eissn>1938-3207</eissn><abstract>Three-dimensional optical (3DO) body scanning has been proposed for automatic anthropometry. However, conventional measurements fail to capture detailed body shape. More sophisticated shape features could better indicate health status.
The objectives were to predict DXA total and regional body composition, serum lipid and diabetes markers, and functional strength from 3DO body scans using statistical shape modeling.
Healthy adults underwent whole-body 3DO and DXA scans, blood tests, and strength assessments in the Shape Up! Adults cross-sectional observational study. Principal component analysis was performed on registered 3DO scans. Stepwise linear regressions were performed to estimate body composition, serum biomarkers, and strength using 3DO principal components (PCs). 3DO model accuracy was compared with simple anthropometric models and precision was compared with DXA.
This analysis included 407 subjects. Eleven PCs for each sex captured 95% of body shape variance. 3DO body composition accuracy to DXA was: fat mass R2 = 0.88 male, 0.93 female; visceral fat mass R2 = 0.67 male, 0.75 female. 3DO body fat test-retest precision was: root mean squared error = 0.81 kg male, 0.66 kg female. 3DO visceral fat was as precise (%CV = 7.4 for males, 6.8 for females) as DXA (%CV = 6.8 for males, 7.4 for females). Multiple 3DO PCs were significantly correlated with serum HDL cholesterol, triglycerides, glucose, insulin, and HOMA-IR, independent of simple anthropometrics. 3DO PCs improved prediction of isometric knee strength (combined model R2 = 0.67 male, 0.59 female; anthropometrics-only model R2 = 0.34 male, 0.24 female).
3DO body shape PCs predict body composition with good accuracy and precision comparable to existing methods. 3DO PCs improve prediction of serum lipid and diabetes markers, and functional strength measurements. The safety and accessibility of 3DO scanning make it appropriate for monitoring individual body composition, and metabolic health and functional strength in epidemiological settings.
This trial was registered at clinicaltrials.gov as NCT03637855.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>31553429</pmid><doi>10.1093/ajcn/nqz218</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-4625-3161</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | The American journal of clinical nutrition, 2019-12, Vol.110 (6), p.1316-1326 |
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| source | MEDLINE; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Absorptiometry, Photon Accuracy Adipose Tissue - diagnostic imaging Adolescent Adult Adults Anthropometry Biomarkers Blood Body Composition Body fat Cholesterol Cross-Sectional Studies Diabetes Diabetes mellitus Dual energy X-ray absorptiometry Editor's Choice Epidemiology Female Females High density lipoprotein Humans imaging Imaging, Three-Dimensional Insulin Insulin - blood Isometric Knee Knee - physiology Lipids Lipoproteins, HDL - blood Male Males Mathematical models Metabolites Middle Aged Model accuracy obesity Original Research Communications principal component analysis Principal components analysis Regression analysis Scanning Statistical methods Strength Three dimensional bodies Triglycerides Triglycerides - blood Young Adult |
| title | Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the Shape Up! studies |
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