Body composition measures assessed by bioelectrical impedance analysis and dual-energy X-ray absorptiometry in a sample of Brazilian adults and older adults

TL;DR

This study compares body composition measurements using bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) in Brazilian adults and older adults, finding significant discrepancies and developing calibration equations to improve BIA accuracy.

Contribution

The study develops and validates new calibration equations to correct BIA estimates of fat-free mass in a diverse Brazilian population.

Findings

BIA overestimates fat-free mass and underestimates fat mass compared to DXA.

Calibration equations significantly improve BIA accuracy when validated against DXA measurements.

The multivariate prediction model effectively aligns BIA-derived fat-free mass with DXA values.

Abstract

Bioelectrical impedance analysis (BIA) is a common technique for assessing body composition in clinical and epidemiological settings. However, its accuracy is limited compared to reference methods such as dual-energy X-ray absorptiometry (DXA).

This study aimed to evaluate the agreement between fat-free mass (FFM) and fat mass (FM) measured using BIA (Tanita BC-418) and DXA and to develop a calibration model to correct BIA estimates in a heterogeneous sample of Brazilian adults and older adults.

We analyzed data from 945 participants (aged≥18 years; 611 female participants) who underwent both BIA and DXA assessments across multiple cross-sectional research projects. Agreement between the BIA and DXA measures of FFM (BIAFFM and DXAFFM) and fat mass (FM) was assessed using Pearson correlation coefficients (r) to evaluate precision and Lin’s concordance correlation coefficients (CCCs) to evaluate accuracy. Mean absolute and relative differences were evaluated using paired t-tests or analysis of variance (ANOVA) by sex, age, and nutritional status based on body mass index (BMI). Linear regression was employed to calibrate BIAFFM against DXAFFM. A multivariate prediction model for DXAFFM was developed using BIA-derived resistance, stature, body mass (BM), and age in a randomly selected subsample comprising 70% of the participants (n = 659) and was validated in the remaining 30% (n = 286).

BIA and DXA measures were highly correlated for both FFM and FM (r = 0.97) and demonstrated moderate to high accuracy (CCC ≥ 0.93). For the entire sample, BIA overestimated FFM by 3.1 kg (SD = 2.4; +7.2%) and underestimated FM by 2.9 kg (2.3; −13.0%) compared to DXA (both p < 0.0001). The resulting calibration equation for FFM was DXAFFM = 0.94420 × BIAFFM–(0.01128 x Age) + 0.20516. The multivariate prediction equation derived from the development group was as follows: FFM (kg) = (Sex × 4.1797) + [Stature (cm) × 0.1062] + [Resistance Index (cm2/Ω) × 0.5289] + [Body Mass (kg) × 0.1797] – [Age (yrs) × 0.0705] – 5.4286 (female participants = 0, male participants = 1). In the validation group, the mean FFM values obtained by the calibrated regression and by the new multivariate equation showed no statistically significant difference from the actual DXAFFM measurement.

Significant discrepancies existed between BIA- and DXA-derived body composition measures in this heterogeneous sample of Brazilian individuals. The developed prediction equations effectively calibrated BIAFFM estimates to align with DXA values, providing a practical method to enhance the accuracy of BIA for body composition assessment in this population.

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