Validity of a 3-compartment body composition model using body volume derived from a novel 2-dimensional image...

Area of Science:

• Body composition analysis
• Anthropometry
• Image analysis

Background:

• Accurate body volume (BV) and relative adiposity (%Fat) estimation are crucial for health assessments.
• Traditional methods like underwater weighing (UWW) are accurate but not always practical.
• Exploring novel, non-invasive techniques for body composition analysis is essential.

Purpose of the Study:

• To compare body volume (BV) estimated via 2D image analysis (BV_IMAGE) and DXA equation (BV_{DXA-Smith-Ryan}) against underwater weighing (BV_UWW).
• To compare relative adiposity (%Fat) derived from 3-compartment (3C) and 4-compartment (4C) models using image analysis and DXA equations against a 4C criterion.

Main Methods:

• Forty-eight healthy young adults (60% male) participated.
• Body volume was measured using a 2D image analysis program (BV_IMAGE) and compared to UWW (BV_UWW).
• Relative adiposity was calculated using 3C (using BV_IMAGE) and 4C models (using DXA-derived BV_{DXA-Smith-Ryan}), compared to a 4C criterion (using BV_UWW).

Main Results:

• BV_IMAGE showed near-perfect correlation (r=0.998) and no significant mean difference compared to BV_UWW.
• %Fat derived from the 4C model using DXA-derived BV ( %Fat_{4C-DXA-Smith-Ryan}) showed no mean difference versus the criterion.
• %Fat derived from the 3C model using image analysis (%Fat_3C-IMAGE) demonstrated small mean differences and superior accuracy (lower SEE and TE) compared to %Fat_{4C-DXA-Smith-Ryan}.

Conclusions:

• A 2D image analysis program provides an accurate and non-invasive estimation of body volume.
• This method allows for reliable estimation of relative adiposity within a 3-compartment model.
• The 2D image analysis approach is a viable tool for body composition assessment in healthy young adults.