Sex-specific equations to estimate body composition: Derivation and validation of diagnostic prediction models using UK Biobank

Abstract

Background & aims: Body mass index and waist circumference are simple measures of obesity. However, they do not distinguish between visceral and subcutaneous fat, or muscle, potentially leading to biased relationships between individual body composition parameters and adverse health outcomes. The purpose of this study was to develop and validate prediction models for volumetric adipose and muscle.

Methods: Based on cross-sectional data of 18,457, 18,260, and 17,052 White adults from the UK Biobank, we developed sex-specific equations to estimate visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), and total thigh fat-free muscle (FFM) volumes, respectively. Volumetric magnetic resonance imaging served as the reference. We used the least absolute shrinkage and selection operator and the extreme gradient boosting methods separately to fit three sequential models, the inputs of which included demographics and anthropometrics and, in some, bioelectrical impedance analysis parameters. We applied comprehensive metrics to assess model performance in the temporal validation set.

Results: The equations that included more predictors generally performed better. Accuracy of the equations was moderate for VAT (percentage of estimates that differed <30% from the measured values, 70 to 78 in males, 64 to 69 in females) and good for ASAT (85 to 91 in males, 90 to 95 in females) and FFM (99 to 100 in both sexes). All the equations appeared precise (interquartile range of the difference, 0.89 to 1.76 L for VAT, 1.16 to 1.61 L for ASAT, 0.81 to 1.39 L for FFM). Bias of all the equations was negligible (-0.17 to 0.05 L for VAT, -0.10 to 0.12 L for ASAT, -0.07 to 0.09 L for FFM). The equations achieved superior cardiometabolic correlations compared with body mass index and waist circumference.

Conclusions: The developed equations to estimate VAT, ASAT, and FFM volumes achieved moderate to good performance. They may be cost-effective tools to revisit the implications of diverse body components.

Keywords: Abdominal subcutaneous adipose tissue; Machine learning; Prediction model; Total thigh fat-free muscle; Visceral adipose tissue; Volumetric magnetic resonance imaging.