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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| Published in | The American journal of clinical nutrition Vol. 110; no. 6; pp. 1316 - 1326 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.12.2019
Oxford University Press American Society for Clinical Nutrition, Inc |
| Subjects | |
| Online Access | Get full text |
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| 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. |
|---|---|
| AbstractList | 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.BACKGROUNDThree-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.OBJECTIVESThe 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.METHODSHealthy 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).RESULTSThis 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.CONCLUSIONS3DO 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. 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 Background 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. Objectives 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. Methods 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. Results 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). Conclusions 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. Background 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. Objectives 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. Methods 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. Results 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). Conclusions 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. 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. |
| Author | Ng, Bennett K Fan, Bo Sommer, Markus J Shepherd, John A Hwaung, Phoenix Nie, Yilin Chow, Dominic Pagano, Ian Bourgeois, Brianna Curless, Brian Wong, Michael C Liu, Yong E Vaisse, Christian Heymsfield, Steven B Kelly, Nisa Kennedy, Samantha Garber, Andrea K |
| AuthorAffiliation | 4 School of Medicine, University of California , San Francisco, CA, USA 3 Pennington Biomedical Research Center, Louisiana State University , Baton Rouge, LA, USA 2 Department of Radiology and Biomedical Imaging, University of California , San Francisco, CA, USA 5 Diabetes Center, University of California , San Francisco, CA, USA 1 University of Hawaii Cancer Center , Honolulu, HI, USA 6 Paul G Allen School of Computer Science and Engineering, University of Washington , Seattle, WA, USA |
| AuthorAffiliation_xml | – name: 3 Pennington Biomedical Research Center, Louisiana State University , Baton Rouge, LA, USA – name: 4 School of Medicine, University of California , San Francisco, CA, USA – name: 6 Paul G Allen School of Computer Science and Engineering, University of Washington , Seattle, WA, USA – name: 2 Department of Radiology and Biomedical Imaging, University of California , San Francisco, CA, USA – name: 1 University of Hawaii Cancer Center , Honolulu, HI, USA – name: 5 Diabetes Center, University of California , San Francisco, CA, USA |
| Author_xml | – sequence: 1 givenname: Bennett K orcidid: 0000-0003-4625-3161 surname: Ng fullname: Ng, Bennett K organization: University of Hawaii Cancer Center, Honolulu, HI, USA – sequence: 2 givenname: Markus J surname: Sommer fullname: Sommer, Markus J organization: Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA – sequence: 3 givenname: Michael C surname: Wong fullname: Wong, Michael C organization: University of Hawaii Cancer Center, Honolulu, HI, USA – sequence: 4 givenname: Ian surname: Pagano fullname: Pagano, Ian organization: University of Hawaii Cancer Center, Honolulu, HI, USA – sequence: 5 givenname: Yilin surname: Nie fullname: Nie, Yilin organization: Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA – sequence: 6 givenname: Bo surname: Fan fullname: Fan, Bo organization: Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA – sequence: 7 givenname: Samantha surname: Kennedy fullname: Kennedy, Samantha organization: Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, USA – sequence: 8 givenname: Brianna surname: Bourgeois fullname: Bourgeois, Brianna organization: Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, USA – sequence: 9 givenname: Nisa surname: Kelly fullname: Kelly, Nisa organization: University of Hawaii Cancer Center, Honolulu, HI, USA – sequence: 10 givenname: Yong E surname: Liu fullname: Liu, Yong E organization: University of Hawaii Cancer Center, Honolulu, HI, USA – sequence: 11 givenname: Phoenix surname: Hwaung fullname: Hwaung, Phoenix organization: Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, USA – sequence: 12 givenname: Andrea K surname: Garber fullname: Garber, Andrea K organization: School of Medicine, University of California, San Francisco, CA, USA – sequence: 13 givenname: Dominic surname: Chow fullname: Chow, Dominic organization: University of Hawaii Cancer Center, Honolulu, HI, USA – sequence: 14 givenname: Christian surname: Vaisse fullname: Vaisse, Christian organization: Diabetes Center, University of California, San Francisco, CA, USA – sequence: 15 givenname: Brian surname: Curless fullname: Curless, Brian organization: Paul G Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA – sequence: 16 givenname: Steven B surname: Heymsfield fullname: Heymsfield, Steven B organization: Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, USA – sequence: 17 givenname: John A surname: Shepherd fullname: Shepherd, John A email: johnshep@hawaii.edu organization: University of Hawaii Cancer Center, Honolulu, HI, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31553429$$D View this record in MEDLINE/PubMed |
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| Copyright | 2019 American Society for Nutrition. Copyright © American Society for Nutrition 2019. 2019 Copyright © American Society for Nutrition 2019. Copyright American Society for Clinical Nutrition, Inc. Dec 2019 |
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| Keywords | strength PBRC UCSF FM RMSE FFM WC imaging PCA SFA 3D body composition PC UHCC BIA diabetes HbA1c obesity principal component analysis 3DO |
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| License | http://www.elsevier.com/open-access/userlicense/1.0 This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Copyright © American Society for Nutrition 2019. |
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| Snippet | Three-dimensional optical (3DO) body scanning has been proposed for automatic anthropometry. However, conventional measurements fail to capture detailed body... ABSTRACT Background Three-dimensional optical (3DO) body scanning has been proposed for automatic anthropometry. However, conventional measurements fail to... Background Three-dimensional optical (3DO) body scanning has been proposed for automatic anthropometry. However, conventional measurements fail to capture... |
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| SubjectTerms | 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 Observational studies 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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