Body composition may be a better predictor of chronic disease risk than body mass index (BMI) in older populations. Objectives: We sought to validate spine fat fraction (%) from dual energy X-ray absorptiometry (DXA) spine scans as a proxy for total abdominal fat. Methods: Total body DXA scan abdominal fat regions of interest (ROI) that have been previously validated by magnetic resonance imaging were assessed among healthy, postmenopausal women who also had antero-posterior spine scans (n = 103). ROIs were (1) lumbar vertebrae L2-L4 and (2) L2-Iliac Crest (L2-IC), manually selected by two independent raters, and (3) trunk, auto-selected by DXA software. Intra-class correlation coefficients evaluated intra and inter-rater reliability on a random subset (N = 25). Linear regression models, validated by bootstrapping, assessed the relationship between spine fat fraction (%) and total abdominal fat (%) ROIs. Results: Mean age, BMI, and total body fat were 66.1 ± 4.8 y, 25.8 ± 3.8 kg/m2 and 40.0 ± 6.6%, respectively. There were no significant differences within or between raters. Linear regression models adjusted for several participant and scan characteristics were equivalent to using only BMI and spine fat fraction. The model predicted L2-L4 (Adj. R2: 0.83) and L2-IC (Adj. R2: 0.84) abdominal fat (%) well; the adjusted R2 for trunk fat (%) was 0.78. Model validation demonstrated minimal over-fitting (Adj. R2: 0.82, 0.83, and 0.77 for L2-L4, L2-IC, and trunk fat, respectively). Conclusions: The strong correlation between spine fat fraction and DXA abdominal fat measures make it suitable for further development in postmenopausal chronic disease risk prediction models. Am. J. Hum. Biol. 28:918–926, 2016.
- body composition
- body mass index
- risk prediction
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics