Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the Shape Up! studies.
body composition
diabetes
imaging
obesity
principal component analysis
strength
Journal
The American journal of clinical nutrition
ISSN: 1938-3207
Titre abrégé: Am J Clin Nutr
Pays: United States
ID NLM: 0376027
Informations de publication
Date de publication:
01 12 2019
01 12 2019
Historique:
received:
22
04
2019
accepted:
07
08
2019
pubmed:
26
9
2019
medline:
3
4
2020
entrez:
26
9
2019
Statut:
ppublish
Résumé
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.
Sections du résumé
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.
Identifiants
pubmed: 31553429
pii: S0002-9165(22)01327-2
doi: 10.1093/ajcn/nqz218
pmc: PMC6885475
doi:
Substances chimiques
Insulin
0
Lipoproteins, HDL
0
Triglycerides
0
Banques de données
ClinicalTrials.gov
['NCT03637855']
Types de publication
Journal Article
Observational Study
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1316-1326Subventions
Organisme : NIDDK NIH HHS
ID : P30 DK072476
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK111698
Pays : United States
Organisme : NIMHD NIH HHS
ID : U54 MD007601
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK040561
Pays : United States
Organisme : NIMHD NIH HHS
ID : U54 MD007584
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK109008
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD082166
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA071789
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © American Society for Nutrition 2019.
Références
Am J Clin Nutr. 2017 Jan;105(1):1-2
pubmed: 28003202
Eur J Clin Nutr. 2017 Nov;71(11):1329-1335
pubmed: 28876331
J Am Coll Nutr. 2015;34(5):367-77
pubmed: 25915106
COPD. 2013 Oct;10(5):597-603
pubmed: 23844827
Obesity (Silver Spring). 2006 Feb;14(2):336-41
pubmed: 16571861
J Clin Densitom. 2013 Jan-Mar;16(1):75-8
pubmed: 23148876
Obesity (Silver Spring). 2014 Mar;22(3):852-62
pubmed: 23613161
Nat Rev Cancer. 2004 Aug;4(8):579-91
pubmed: 15286738
Clin Chem. 1972 Jun;18(6):499-502
pubmed: 4337382
Am J Clin Nutr. 2004 Mar;79(3):379-84
pubmed: 14985210
IEEE Trans Pattern Anal Mach Intell. 2010 Dec;32(12):2262-75
pubmed: 20975122
Am J Clin Nutr. 2006 Aug;84(2):449-60
pubmed: 16895897
Obes Rev. 2012 Mar;13(3):275-86
pubmed: 22106927
Eur J Clin Nutr. 2016 Nov;70(11):1265-1270
pubmed: 27329614
J Bone Miner Res. 1996 May;11(5):626-37
pubmed: 9157777
Circulation. 2008 Apr 1;117(13):1658-67
pubmed: 18362231
J Clin Endocrinol Metab. 2011 Jun;96(6):1654-63
pubmed: 21602457
Diabetes Care. 2018 Oct;41(10):2195-2201
pubmed: 30061315
Obes Surg. 2011 Jan;21(1):42-7
pubmed: 20563664
J Clin Epidemiol. 2002 Aug;55(8):757-66
pubmed: 12384189