Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity
The obesity epidemic brought a need for accessible methods to monitor body composition, as excess adiposity has been associated with cardiovascular disease, metabolic disorders, and some cancers. Recent 3-dimensional optical (3DO) imaging advancements have provided opportunities for assessing body c...
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Veröffentlicht in: | The American journal of clinical nutrition 2023-09, Vol.118 (3), p.657-671 |
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creator | Wong, Michael C. Bennett, Jonathan P. Quon, Brandon Leong, Lambert T. Tian, Isaac Y. Liu, Yong E. Kelly, Nisa N. McCarthy, Cassidy Chow, Dominic Pujades, Sergi Garber, Andrea K. Maskarinec, Gertraud Heymsfield, Steven B. Shepherd, John A. |
description | The obesity epidemic brought a need for accessible methods to monitor body composition, as excess adiposity has been associated with cardiovascular disease, metabolic disorders, and some cancers. Recent 3-dimensional optical (3DO) imaging advancements have provided opportunities for assessing body composition. However, the accuracy and precision of an overall 3DO body composition model in specific subgroups are unknown.
This study aimed to evaluate 3DO’s accuracy and precision by subgroups of age, body mass index, and ethnicity.
A cross-sectional analysis was performed using data from the Shape Up! Adults study. Each participant received duplicate 3DO and dual-energy X-ray absorptiometry (DXA) scans. 3DO meshes were digitally registered and reposed using Meshcapade. Principal component analysis was performed on 3DO meshes. The resulting principal components estimated DXA whole-body and regional body composition using stepwise forward linear regression with 5-fold cross-validation. Duplicate 3DO and DXA scans were used for test–retest precision. Student’s t tests were performed between 3DO and DXA by subgroup to determine significant differences.
Six hundred thirty-four participants (females = 346) had completed the study at the time of the analysis. 3DO total fat mass in the entire sample achieved R2 of 0.94 with root mean squared error (RMSE) of 2.91 kg compared to DXA in females and similarly in males. 3DO total fat mass achieved a % coefficient of variation (RMSE) of 1.76% (0.44 kg), whereas DXA was 0.98% (0.24 kg) in females and similarly in males. There were no mean differences for total fat, fat-free, percent fat, or visceral adipose tissue by age group (P > 0.068). However, there were mean differences for underweight, Asian, and Black females as well as Native Hawaiian or other Pacific Islanders (P < 0.038).
A single 3DO body composition model produced accurate and precise body composition estimates that can be used on diverse populations. However, adjustments to specific subgroups may be warranted to improve the accuracy in those that had significant differences.
This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults). |
doi_str_mv | 10.1016/j.ajcnut.2023.07.010 |
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This study aimed to evaluate 3DO’s accuracy and precision by subgroups of age, body mass index, and ethnicity.
A cross-sectional analysis was performed using data from the Shape Up! Adults study. Each participant received duplicate 3DO and dual-energy X-ray absorptiometry (DXA) scans. 3DO meshes were digitally registered and reposed using Meshcapade. Principal component analysis was performed on 3DO meshes. The resulting principal components estimated DXA whole-body and regional body composition using stepwise forward linear regression with 5-fold cross-validation. Duplicate 3DO and DXA scans were used for test–retest precision. Student’s t tests were performed between 3DO and DXA by subgroup to determine significant differences.
Six hundred thirty-four participants (females = 346) had completed the study at the time of the analysis. 3DO total fat mass in the entire sample achieved R2 of 0.94 with root mean squared error (RMSE) of 2.91 kg compared to DXA in females and similarly in males. 3DO total fat mass achieved a % coefficient of variation (RMSE) of 1.76% (0.44 kg), whereas DXA was 0.98% (0.24 kg) in females and similarly in males. There were no mean differences for total fat, fat-free, percent fat, or visceral adipose tissue by age group (P > 0.068). However, there were mean differences for underweight, Asian, and Black females as well as Native Hawaiian or other Pacific Islanders (P < 0.038).
A single 3DO body composition model produced accurate and precise body composition estimates that can be used on diverse populations. However, adjustments to specific subgroups may be warranted to improve the accuracy in those that had significant differences.
This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults).</description><identifier>ISSN: 0002-9165</identifier><identifier>ISSN: 1938-3207</identifier><identifier>EISSN: 1938-3207</identifier><identifier>DOI: 10.1016/j.ajcnut.2023.07.010</identifier><identifier>PMID: 37474106</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Absorptiometry, Photon - methods ; Accuracy ; Adipose tissue ; Adult ; Age ; Body Composition ; Body fat ; Body mass ; Body Mass Index ; Body size ; Cardiovascular diseases ; Coefficient of variation ; Computer Science ; Computer Vision and Pattern Recognition ; Cross-Sectional Studies ; diversity ; Dual energy X-ray absorptiometry ; DXA ; Epidemics ; Ethnicity ; Female ; Females ; Human health and pathology ; Humans ; Life Sciences ; Male ; Males ; Medical imaging ; Metabolic disorders ; Minority & ethnic groups ; Monitoring methods ; Obesity - diagnostic imaging ; Optical Imaging ; Original ; Principal components analysis ; Root-mean-square errors ; Subgroups ; three-dimensional optical ; Underweight</subject><ispartof>The American journal of clinical nutrition, 2023-09, Vol.118 (3), p.657-671</ispartof><rights>2023 American Society for Nutrition</rights><rights>Copyright © 2023 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.</rights><rights>Copyright American Society for Clinical Nutrition, Inc. Sep 2023</rights><rights>Attribution - NonCommercial - ShareAlike</rights><rights>2023 American Society for Nutrition. Published by Elsevier Inc. All rights reserved. 2023 American Society for Nutrition</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-8db2484f9cfb0ef2e2b8102771dd950784c1d32e08f55f126767c8557109275b3</citedby><cites>FETCH-LOGICAL-c526t-8db2484f9cfb0ef2e2b8102771dd950784c1d32e08f55f126767c8557109275b3</cites><orcidid>0000-0003-2392-9253 ; 0000-0002-9604-7721</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/37474106$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://inria.hal.science/hal-04397544$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Wong, Michael C.</creatorcontrib><creatorcontrib>Bennett, Jonathan P.</creatorcontrib><creatorcontrib>Quon, Brandon</creatorcontrib><creatorcontrib>Leong, Lambert T.</creatorcontrib><creatorcontrib>Tian, Isaac Y.</creatorcontrib><creatorcontrib>Liu, Yong E.</creatorcontrib><creatorcontrib>Kelly, Nisa N.</creatorcontrib><creatorcontrib>McCarthy, Cassidy</creatorcontrib><creatorcontrib>Chow, Dominic</creatorcontrib><creatorcontrib>Pujades, Sergi</creatorcontrib><creatorcontrib>Garber, Andrea K.</creatorcontrib><creatorcontrib>Maskarinec, Gertraud</creatorcontrib><creatorcontrib>Heymsfield, Steven B.</creatorcontrib><creatorcontrib>Shepherd, John A.</creatorcontrib><title>Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity</title><title>The American journal of clinical nutrition</title><addtitle>Am J Clin Nutr</addtitle><description>The obesity epidemic brought a need for accessible methods to monitor body composition, as excess adiposity has been associated with cardiovascular disease, metabolic disorders, and some cancers. Recent 3-dimensional optical (3DO) imaging advancements have provided opportunities for assessing body composition. However, the accuracy and precision of an overall 3DO body composition model in specific subgroups are unknown.
This study aimed to evaluate 3DO’s accuracy and precision by subgroups of age, body mass index, and ethnicity.
A cross-sectional analysis was performed using data from the Shape Up! Adults study. Each participant received duplicate 3DO and dual-energy X-ray absorptiometry (DXA) scans. 3DO meshes were digitally registered and reposed using Meshcapade. Principal component analysis was performed on 3DO meshes. The resulting principal components estimated DXA whole-body and regional body composition using stepwise forward linear regression with 5-fold cross-validation. Duplicate 3DO and DXA scans were used for test–retest precision. Student’s t tests were performed between 3DO and DXA by subgroup to determine significant differences.
Six hundred thirty-four participants (females = 346) had completed the study at the time of the analysis. 3DO total fat mass in the entire sample achieved R2 of 0.94 with root mean squared error (RMSE) of 2.91 kg compared to DXA in females and similarly in males. 3DO total fat mass achieved a % coefficient of variation (RMSE) of 1.76% (0.44 kg), whereas DXA was 0.98% (0.24 kg) in females and similarly in males. There were no mean differences for total fat, fat-free, percent fat, or visceral adipose tissue by age group (P > 0.068). However, there were mean differences for underweight, Asian, and Black females as well as Native Hawaiian or other Pacific Islanders (P < 0.038).
A single 3DO body composition model produced accurate and precise body composition estimates that can be used on diverse populations. However, adjustments to specific subgroups may be warranted to improve the accuracy in those that had significant differences.
This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults).</description><subject>Absorptiometry, Photon - methods</subject><subject>Accuracy</subject><subject>Adipose tissue</subject><subject>Adult</subject><subject>Age</subject><subject>Body Composition</subject><subject>Body fat</subject><subject>Body mass</subject><subject>Body Mass Index</subject><subject>Body size</subject><subject>Cardiovascular diseases</subject><subject>Coefficient of variation</subject><subject>Computer Science</subject><subject>Computer Vision and Pattern Recognition</subject><subject>Cross-Sectional Studies</subject><subject>diversity</subject><subject>Dual energy X-ray absorptiometry</subject><subject>DXA</subject><subject>Epidemics</subject><subject>Ethnicity</subject><subject>Female</subject><subject>Females</subject><subject>Human health and pathology</subject><subject>Humans</subject><subject>Life Sciences</subject><subject>Male</subject><subject>Males</subject><subject>Medical imaging</subject><subject>Metabolic disorders</subject><subject>Minority & ethnic groups</subject><subject>Monitoring methods</subject><subject>Obesity - diagnostic imaging</subject><subject>Optical Imaging</subject><subject>Original</subject><subject>Principal components analysis</subject><subject>Root-mean-square errors</subject><subject>Subgroups</subject><subject>three-dimensional optical</subject><subject>Underweight</subject><issn>0002-9165</issn><issn>1938-3207</issn><issn>1938-3207</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kUFv1DAQhS0EokvhHyAUiQtITRg7cZxcQNtVoSstKgc4W47jZB0l9mInK-Xf45BSQQ-cRra_92Y8D6HXGBIMOP_QJaKTZhoTAiRNgCWA4Qna4DIt4pQAe4o2AEDiEuf0Ar3wvgPAJCvy5-giZRnLMOQb1G6lnJyQcyRMHX1zSmqvrYlsE6VxrQdllqPoo7vTqGWo-0G02rRRY110bes52tnhZL0eF1U1R9tWXUXXX_dXvw1vxqPRUo_zS_SsEb1Xr-7rJfrx-eb77jY-3H3Z77aHWFKSj3FRV2HErCllU4FqiCJVgYEwhuu6pMCKTOI6JQqKhtIGk5zlTBaUMgwlYbRKL9Gn1fc0VYOqpTKjEz0_OT0IN3MrNP_3xegjb-2ZY6CYEYyDw_vV4fhId7s98OUOsrRkNMvOC_vuvpuzPyflRz5oL1XfC6Ps5DkpwpYJpSUN6NtHaGcnFza7UDmkjOIyD1S2UtJZ751qHibAwJfYecfX2PkSOwfGQ-xB9ubvXz-I_uQcgI8roMLuz1o57qVWRqpah8hHXlv9_w6_AD67vbU</recordid><startdate>20230901</startdate><enddate>20230901</enddate><creator>Wong, Michael C.</creator><creator>Bennett, Jonathan P.</creator><creator>Quon, Brandon</creator><creator>Leong, Lambert T.</creator><creator>Tian, Isaac Y.</creator><creator>Liu, Yong E.</creator><creator>Kelly, Nisa N.</creator><creator>McCarthy, Cassidy</creator><creator>Chow, Dominic</creator><creator>Pujades, Sergi</creator><creator>Garber, Andrea K.</creator><creator>Maskarinec, Gertraud</creator><creator>Heymsfield, Steven B.</creator><creator>Shepherd, John A.</creator><general>Elsevier Inc</general><general>American Society for Clinical Nutrition, Inc</general><general>Oxford University Press</general><general>American Society for Nutrition</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>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>1XC</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-2392-9253</orcidid><orcidid>https://orcid.org/0000-0002-9604-7721</orcidid></search><sort><creationdate>20230901</creationdate><title>Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity</title><author>Wong, Michael C. ; Bennett, Jonathan P. ; Quon, Brandon ; Leong, Lambert T. ; Tian, Isaac Y. ; Liu, Yong E. ; Kelly, Nisa N. ; McCarthy, Cassidy ; Chow, Dominic ; Pujades, Sergi ; Garber, Andrea K. ; Maskarinec, Gertraud ; Heymsfield, Steven B. ; Shepherd, John A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c526t-8db2484f9cfb0ef2e2b8102771dd950784c1d32e08f55f126767c8557109275b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Absorptiometry, Photon - methods</topic><topic>Accuracy</topic><topic>Adipose tissue</topic><topic>Adult</topic><topic>Age</topic><topic>Body Composition</topic><topic>Body fat</topic><topic>Body mass</topic><topic>Body Mass Index</topic><topic>Body size</topic><topic>Cardiovascular diseases</topic><topic>Coefficient of variation</topic><topic>Computer Science</topic><topic>Computer Vision and Pattern Recognition</topic><topic>Cross-Sectional Studies</topic><topic>diversity</topic><topic>Dual energy X-ray absorptiometry</topic><topic>DXA</topic><topic>Epidemics</topic><topic>Ethnicity</topic><topic>Female</topic><topic>Females</topic><topic>Human health and pathology</topic><topic>Humans</topic><topic>Life Sciences</topic><topic>Male</topic><topic>Males</topic><topic>Medical imaging</topic><topic>Metabolic disorders</topic><topic>Minority & ethnic groups</topic><topic>Monitoring methods</topic><topic>Obesity - diagnostic imaging</topic><topic>Optical Imaging</topic><topic>Original</topic><topic>Principal components analysis</topic><topic>Root-mean-square errors</topic><topic>Subgroups</topic><topic>three-dimensional optical</topic><topic>Underweight</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wong, Michael C.</creatorcontrib><creatorcontrib>Bennett, Jonathan P.</creatorcontrib><creatorcontrib>Quon, Brandon</creatorcontrib><creatorcontrib>Leong, Lambert T.</creatorcontrib><creatorcontrib>Tian, Isaac Y.</creatorcontrib><creatorcontrib>Liu, Yong E.</creatorcontrib><creatorcontrib>Kelly, Nisa N.</creatorcontrib><creatorcontrib>McCarthy, Cassidy</creatorcontrib><creatorcontrib>Chow, Dominic</creatorcontrib><creatorcontrib>Pujades, Sergi</creatorcontrib><creatorcontrib>Garber, Andrea K.</creatorcontrib><creatorcontrib>Maskarinec, Gertraud</creatorcontrib><creatorcontrib>Heymsfield, Steven B.</creatorcontrib><creatorcontrib>Shepherd, John A.</creatorcontrib><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>Hyper Article en Ligne (HAL)</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>Wong, Michael C.</au><au>Bennett, Jonathan P.</au><au>Quon, Brandon</au><au>Leong, Lambert T.</au><au>Tian, Isaac Y.</au><au>Liu, Yong E.</au><au>Kelly, Nisa N.</au><au>McCarthy, Cassidy</au><au>Chow, Dominic</au><au>Pujades, Sergi</au><au>Garber, Andrea K.</au><au>Maskarinec, Gertraud</au><au>Heymsfield, Steven B.</au><au>Shepherd, John A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity</atitle><jtitle>The American journal of clinical nutrition</jtitle><addtitle>Am J Clin Nutr</addtitle><date>2023-09-01</date><risdate>2023</risdate><volume>118</volume><issue>3</issue><spage>657</spage><epage>671</epage><pages>657-671</pages><issn>0002-9165</issn><issn>1938-3207</issn><eissn>1938-3207</eissn><abstract>The obesity epidemic brought a need for accessible methods to monitor body composition, as excess adiposity has been associated with cardiovascular disease, metabolic disorders, and some cancers. Recent 3-dimensional optical (3DO) imaging advancements have provided opportunities for assessing body composition. However, the accuracy and precision of an overall 3DO body composition model in specific subgroups are unknown.
This study aimed to evaluate 3DO’s accuracy and precision by subgroups of age, body mass index, and ethnicity.
A cross-sectional analysis was performed using data from the Shape Up! Adults study. Each participant received duplicate 3DO and dual-energy X-ray absorptiometry (DXA) scans. 3DO meshes were digitally registered and reposed using Meshcapade. Principal component analysis was performed on 3DO meshes. The resulting principal components estimated DXA whole-body and regional body composition using stepwise forward linear regression with 5-fold cross-validation. Duplicate 3DO and DXA scans were used for test–retest precision. Student’s t tests were performed between 3DO and DXA by subgroup to determine significant differences.
Six hundred thirty-four participants (females = 346) had completed the study at the time of the analysis. 3DO total fat mass in the entire sample achieved R2 of 0.94 with root mean squared error (RMSE) of 2.91 kg compared to DXA in females and similarly in males. 3DO total fat mass achieved a % coefficient of variation (RMSE) of 1.76% (0.44 kg), whereas DXA was 0.98% (0.24 kg) in females and similarly in males. There were no mean differences for total fat, fat-free, percent fat, or visceral adipose tissue by age group (P > 0.068). However, there were mean differences for underweight, Asian, and Black females as well as Native Hawaiian or other Pacific Islanders (P < 0.038).
A single 3DO body composition model produced accurate and precise body composition estimates that can be used on diverse populations. However, adjustments to specific subgroups may be warranted to improve the accuracy in those that had significant differences.
This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults).</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>37474106</pmid><doi>10.1016/j.ajcnut.2023.07.010</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0003-2392-9253</orcidid><orcidid>https://orcid.org/0000-0002-9604-7721</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Absorptiometry, Photon - methods Accuracy Adipose tissue Adult Age Body Composition Body fat Body mass Body Mass Index Body size Cardiovascular diseases Coefficient of variation Computer Science Computer Vision and Pattern Recognition Cross-Sectional Studies diversity Dual energy X-ray absorptiometry DXA Epidemics Ethnicity Female Females Human health and pathology Humans Life Sciences Male Males Medical imaging Metabolic disorders Minority & ethnic groups Monitoring methods Obesity - diagnostic imaging Optical Imaging Original Principal components analysis Root-mean-square errors Subgroups three-dimensional optical Underweight |
title | Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity |
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