Quantifying childhood fat mass: comparison of a novel height-and-weight-based prediction approach with DXA and bioelectrical impedance.
Mohammed T HuddaChristopher G OwenAlicja Regina RudnickaDerek G CookPeter H WhincupClaire M NightingalePublished in: International journal of obesity (2005) (2020)
Accurate assessment of childhood adiposity is important both for individuals and populations. We compared fat mass (FM) predictions from a novel prediction model based on height, weight and demographic factors (height-weight equation) with FM from bioelectrical impedance (BIA) and dual-energy X-ray absorptiometry (DXA), using the deuterium dilution method as a reference standard. FM data from all four methods were available for 174 ALSPAC Study participants, seen 2002-2003, aged 11-12-years. FM predictions from the three approaches were compared to the reference standard using; R2, calibration (slope and intercept) and root mean square error (RMSE). R2 values were high from 'height-weight equation' (90%) but lower than from DXA (95%) and BIA (91%). Whilst calibration intercepts from all three approaches were close to the ideal of 0, the calibration slope from the 'height-weight equation' (slope = 1.02) was closer to the ideal of 1 than DXA (slope = 0.88) and BIA (slope = 0.87) assessments. The 'height-weight equation' provided more accurate individual predictions with a smaller RMSE value (2.6 kg) than BIA (3.1 kg) or DXA (3.4 kg). Predictions from the 'height-weight equation' were at least as accurate as DXA and BIA and were based on simpler measurements and open-source equation, emphasising its potential for both individual and population-level FM assessments.
Keyphrases
- dual energy
- body mass index
- computed tomography
- weight gain
- body composition
- image quality
- physical activity
- weight loss
- contrast enhanced
- bone mineral density
- high resolution
- body weight
- type diabetes
- metabolic syndrome
- magnetic resonance
- early life
- postmenopausal women
- fatty acid
- machine learning
- deep learning
- liquid chromatography tandem mass spectrometry
- big data
- ms ms
- electron microscopy
- solid phase extraction