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Modeling the shape and composition of the human body using dual energy X-ray absorptiometry images.

Abstract There is growing evidence that body shape and regional body composition are strong indicators of metabolic health. The purpose of this study was to develop statistical models that accurately describe holistic body shape, thickness, and leanness. We hypothesized that there are unique body shape features that are predictive of mortality beyond standard clinical measures. We developed algorithms to process whole-body dual-energy X-ray absorptiometry (DXA) scans into body thickness and leanness images. We performed statistical appearance modeling (SAM) and principal component analysis (PCA) to efficiently encode the variance of body shape, leanness, and thickness across sample of 400 older Americans from the Health ABC study. The sample included 200 cases and 200 controls based on 6-year mortality status, matched on sex, race and BMI. The final model contained 52 points outlining the torso, upper arms, thighs, and bony landmarks. Correlation analyses were performed on the PCA parameters to identify body shape features that vary across groups and with metabolic risk. Stepwise logistic regression was performed to identify sex and race, and predict mortality risk as a function of body shape parameters. These parameters are novel body composition features that uniquely identify body phenotypes of different groups and predict mortality risk. Three parameters from a SAM of body leanness and thickness accurately identified sex (training AUC = 0.99) and six accurately identified race (training AUC = 0.91) in the sample dataset. Three parameters from a SAM of only body thickness predicted mortality (training AUC = 0.66, validation AUC = 0.62). Further study is warranted to identify specific shape/composition features that predict other health outcomes.
PMID
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Authors

Mayor MeshTerms
Keywords
Journal Title plos one
Publication Year Start




PMID- 28423041
OWN - NLM
STAT- In-Process
DA  - 20170419
LR  - 20170419
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 12
IP  - 4
DP  - 2017
TI  - Modeling the shape and composition of the human body using dual energy X-ray
      absorptiometry images.
PG  - e0175857
LID - 10.1371/journal.pone.0175857 [doi]
AB  - There is growing evidence that body shape and regional body composition are
      strong indicators of metabolic health. The purpose of this study was to develop
      statistical models that accurately describe holistic body shape, thickness, and
      leanness. We hypothesized that there are unique body shape features that are
      predictive of mortality beyond standard clinical measures. We developed
      algorithms to process whole-body dual-energy X-ray absorptiometry (DXA) scans
      into body thickness and leanness images. We performed statistical appearance
      modeling (SAM) and principal component analysis (PCA) to efficiently encode the
      variance of body shape, leanness, and thickness across sample of 400 older
      Americans from the Health ABC study. The sample included 200 cases and 200
      controls based on 6-year mortality status, matched on sex, race and BMI. The
      final model contained 52 points outlining the torso, upper arms, thighs, and bony
      landmarks. Correlation analyses were performed on the PCA parameters to identify 
      body shape features that vary across groups and with metabolic risk. Stepwise
      logistic regression was performed to identify sex and race, and predict mortality
      risk as a function of body shape parameters. These parameters are novel body
      composition features that uniquely identify body phenotypes of different groups
      and predict mortality risk. Three parameters from a SAM of body leanness and
      thickness accurately identified sex (training AUC = 0.99) and six accurately
      identified race (training AUC = 0.91) in the sample dataset. Three parameters
      from a SAM of only body thickness predicted mortality (training AUC = 0.66,
      validation AUC = 0.62). Further study is warranted to identify specific
      shape/composition features that predict other health outcomes.
FAU - Shepherd, John A
AU  - Shepherd JA
AD  - Department of Radiology & Biomedical Imaging, University of California San
      Francisco, San Francisco, California, United States of America.
AD  - Graduate Program in Bioengineering, University of California San Francisco, San
      Francisco, California, United States of America.
AD  - Graduate Program in Bioengineering, University of California, Berkeley,
      California, United States of America.
FAU - Ng, Bennett K
AU  - Ng BK
AUID- ORCID: http://orcid.org/0000-0003-4625-3161
AD  - Department of Radiology & Biomedical Imaging, University of California San
      Francisco, San Francisco, California, United States of America.
AD  - Graduate Program in Bioengineering, University of California San Francisco, San
      Francisco, California, United States of America.
AD  - Graduate Program in Bioengineering, University of California, Berkeley,
      California, United States of America.
FAU - Fan, Bo
AU  - Fan B
AD  - Department of Radiology & Biomedical Imaging, University of California San
      Francisco, San Francisco, California, United States of America.
FAU - Schwartz, Ann V
AU  - Schwartz AV
AD  - Department of Epidemiology and Biostatistics, University of California San
      Francisco, San Francisco, California, United States of America.
FAU - Cawthon, Peggy
AU  - Cawthon P
AD  - San Francisco Coordinating Center, California Pacific Medical Center Research
      Institute, San Francisco, California, United States of America.
FAU - Cummings, Steven R
AU  - Cummings SR
AD  - San Francisco Coordinating Center, California Pacific Medical Center Research
      Institute, San Francisco, California, United States of America.
FAU - Kritchevsky, Stephen
AU  - Kritchevsky S
AD  - San Francisco Coordinating Center, California Pacific Medical Center Research
      Institute, San Francisco, California, United States of America.
FAU - Nevitt, Michael
AU  - Nevitt M
AD  - Department of Epidemiology and Biostatistics, University of California San
      Francisco, San Francisco, California, United States of America.
FAU - Santanasto, Adam
AU  - Santanasto A
AD  - Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania,
      United States of America.
FAU - Cootes, Timothy F
AU  - Cootes TF
AD  - Centre for Imaging Sciences, University of Manchester, Manchester, United
      Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20170419
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
EDAT- 2017/04/20 06:00
MHDA- 2017/04/20 06:00
CRDT- 2017/04/20 06:00
PHST- 2016/11/03 [received]
PHST- 2017/03/31 [accepted]
AID - 10.1371/journal.pone.0175857 [doi]
AID - PONE-D-16-43776 [pii]
PST - epublish
SO  - PLoS One. 2017 Apr 19;12(4):e0175857. doi: 10.1371/journal.pone.0175857.
      eCollection 2017.

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