Dual energy X-ray absorptiometry spine scans to determine abdominal fat in postmenopausal women

• Published in: American journal of human biology Vol. 28; no. 6; pp. 918 - 926
• Main Authors: Bea, J.W., Blew, R.M., Going, S.B., Hsu, C.-H., Lee, M.C., Lee, V.R., Caan, B.J., Kwan, M.L., Lohman, T.G.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.11.2016; Wiley Subscription Services, Inc
• Subjects: Abdomen; Abdominal Fat - diagnostic imaging; Absorptiometry, Photon - methods; adiposity; Aged; Arizona; Body composition; Body fat; Body mass; Body Mass Index; Body size; Chronic illnesses; Correlation coefficients; Dual energy X-ray absorptiometry; Female; Health risk assessment; Humans; Iliac crest; Magnetic resonance imaging; Middle Aged; obesity; Post-menopause; Postmenopause; Prediction models; Regression analysis; Regression models; Reliability analysis; Reproducibility of Results; risk prediction; Software reliability; Spine; Spine (lumbar); Vertebrae; adiposity; body composition; body mass index; risk prediction; obesity
• ISSN: 1042-0533; 1520-6300
• DOI: 10.1002/ajhb.22892

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. © 2016Wiley Periodicals, Inc.