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Peer-reviewedCC BY 4.0Obesity (Silver Spring, Md.)

Compositional Data Analysis of Relative Body Mass and Physical Function Using the Relative Shift Approach

  • Daniel P. Beavers1
  • Lauren S. Roe2
  • Yutong Li1
  • Katarina L. Dobrosky1
  • Ian Janssen3
  • Kristen M. Beavers4
4 affiliations
  1. 1Department of Statistical Sciences Wake Forest University Winston‐Salem North Carolina USA
  2. 2Department of Epidemiology University of Pittsburgh School of Public Health Pittsburgh Pennsylvania USA
  3. 3Department of Public Health Sciences and School of Kinesiology and Health Studies Queens University Kingston Ontario Canada
  4. 4Department of Internal Medicine, Section of Gerontology and Geriatric Medicine Wake Forest University School of Medicine Winston‐Salem North Carolina USA
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Abstract

Objective: The purpose of this study was to present the novel relative shift approach for compositional data analysis of body composition as a predictor for gait speed and to compare it to existing methods.

Methods: Body composition was quantified from dual‐energy x‐ray absorptiometry in 145 older adults living with obesity. Gait speed was measured from a fast‐paced 400‐m walk. The associations between speed and body composition used separate least squares regression models with absolute (kg) mass and compositional methods based on isometric log‐ratio (ILR) transformations and the relative shift approach.

Results: All methods found significant associations between gait speed and body composition. The ILR approach was fit three times, once for each component variable (lean, fat, and bone mass). Predictions differed for the same magnitude of 5% composition change due to total body mass differences (+4.4 [0.8 to 8.0] vs. +5.0 [1.2 to 8.8] cm/s). The absolute mass model ignored the relative nature of the components. The relative shift model required no transformation and provided consistent predictions for 5% change (+4.6 [1.0 to 8.2] cm/s).

Conclusions: Our data suggest the relative shift model may provide simpler estimation and interpretation than ILR for models with compositional covariates.

Trial Registration: ClinicalTrials.gov identifier NCT04076618

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Cite

BibTeXRIS
BibTeX
@article{beavers2026compositional,
  title = {{Compositional Data Analysis of Relative Body Mass and Physical Function Using the Relative Shift Approach}},
  author = {Daniel P. Beavers and Lauren S. Roe and Yutong Li and Katarina L. Dobrosky and Ian Janssen and Kristen M. Beavers},
  journal = {Obesity (Silver Spring, Md.)},
  year = {2026},
  month = oct,
  doi = {10.1002/oby.70307},
  url = {https://doi.org/10.1002/oby.70307},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Compositional Data Analysis of Relative Body Mass and Physical Function Using the Relative Shift Approach
AU  - Daniel P. Beavers
AU  - Lauren S. Roe
AU  - Yutong Li
AU  - Katarina L. Dobrosky
AU  - Ian Janssen
AU  - Kristen M. Beavers
PY  - 2026
DA  - 2026/10/06
JO  - Obesity (Silver Spring, Md.)
DO  - 10.1002/oby.70307
UR  - https://doi.org/10.1002/oby.70307
AB  - Objective: The purpose of this study was to present the novel relative shift approach for compositional data analysis of body composition as a predictor for gait speed and to compare it to existing methods.  Methods: Body composition was quantified from dual‐energy x‐ray absorptiometry in 145 older adults living with obesity. Gait speed was measured from a fast‐paced 400‐m walk. The associations between speed and body composition used separate least squares regression models with absolute (kg) mass and compositional methods based on isometric log‐ratio (ILR) transformations and the relative shift approach.  Results: All methods found significant associations between gait speed and body composition. The ILR approach was fit three times, once for each component variable (lean, fat, and bone mass). Predictions differed for the same magnitude of 5% composition change due to total body mass differences (+4.4 [0.8 to 8.0] vs. +5.0 [1.2 to 8.8] cm/s). The absolute mass model ignored the relative nature of the components. The relative shift model required no transformation and provided consistent predictions for 5% change (+4.6 [1.0 to 8.2] cm/s).  Conclusions: Our data suggest the relative shift model may provide simpler estimation and interpretation than ILR for models with compositional covariates.  Trial Registration: ClinicalTrials.gov identifier NCT04076618
SN  - 1930-7381
SN  - 1930-739X
ER  -