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Determining body composition using different bioimpedance technologies: Is an agreement possible?

Francesco Campa1, A Sampieri1, G Cerullo1

  • 1Department of Biomedical Sciences, University of Padua, Padua, Italy.

Clinical Nutrition (Edinburgh, Scotland)
|November 9, 2025
PubMed
Summary
This summary is machine-generated.

Bioelectrical impedance analysis (BIA) technologies show differences in raw measurements, but fat-free mass (FFM) estimates are comparable across devices when using population-specific equations. However, fat mass (FM) derived from FFM lacks individual-level accuracy.

Keywords:
BIADXAFat massFat-free massPhase angle

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Area of Science:

  • Body composition analysis
  • Biomedical engineering
  • Human physiology

Background:

  • Bioelectrical impedance analysis (BIA) technologies exhibit variability in measuring resistance (R), reactance (Xc), and phase angle (PhA).
  • The impact of this technological variability on body composition estimates, specifically fat-free mass (FFM) and fat mass (FM), remains unclear.

Purpose of the Study:

  • To evaluate the agreement of FFM estimates from different BIA technologies against dual-energy X-ray absorptiometry (DXA).
  • To assess the agreement of BIA-derived FM, indirectly calculated from FFM, against DXA.
  • To investigate the role of predictive equations in standardizing BIA-based body composition measurements.

Main Methods:

  • 288 adults (167 men, 121 women) underwent whole-body foot-to-hand and direct segmental BIA (50 kHz) measurements.
  • Dual-energy X-ray absorptiometry (DXA) served as the reference method for body composition.
  • Predictive equations for FFM were developed and validated using stepwise regression; agreement was assessed using Bland-Altman analysis and Lin's concordance correlation coefficient.

Main Results:

  • Foot-to-hand BIA showed lower R but higher Xc and PhA compared to direct segmental BIA (p < 0.001).
  • Despite differences in raw parameters, FFM estimates demonstrated no significant bias across BIA devices and showed high agreement with DXA.
  • While group-level FM derived from FFM agreed with DXA, individual-level assessment revealed systematic trends.

Conclusions:

  • Comparable FFM estimates can be achieved across different BIA technologies when predictive equations are derived within the same population and reference method.
  • Indirectly obtained FM from FFM lacks individual-level accuracy, highlighting limitations in current BIA applications for precise fat mass determination.