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Long-Term Precision of Dual-Energy X-ray Absorptiometry Body Composition Measurements and Association With Their Covariates

Journal of Clinical Densitometry
DOI: 10.1016/j.jocd.2013.09.010
  • Body Composition
  • Bone Mineral Density
  • Hologic
  • Precision
  • Whole Body


Abstract Few studies have described the long-term repeatability of dual-energy X-ray absorptiometry scans. Even fewer studies have been performed with enough participants to identify possible precision covariates such as sex, age, and body mass index (BMI). Our objective was to investigate the long-term repeatability of both total and subregional body composition measurements and their associations with covariates in a large sample. Two valid whole-body dual-energy X-ray absorptiometry scans were available for 609 participants in the National Health and Nutrition Examination Survey 2000–2002. Participants with scan-quality issues were excluded. Participants varied in race and ethnicity, sex, age (mean 38.8 ± 17.5; range 16–69 yr), and BMI (mean, 26.9 ± 5.2; range 14.1–43.5 kg/m2). The length of time between scans ranged from 3 to 51 days (mean, 18.7 ± 8.4). Precision error estimates for total body measures (bone mineral density, bone mineral content, lean mass, total mass, fat mass, and percent body fat) were calculated as root mean square percent coefficients of variation and standard deviations. The average root mean square percent coefficients of variation and root mean square standard deviations of the precision error for total body variables were 1.12 and 0.01 g/cm2 for bone mineral density, 1.14 and 27.3 g for bone mineral content, 1.97 and 505 g for fat mass, 1.46 and 760 g for lean mass, 1.10 and 858 g for total mass, and 1.80 and 0.59 for percent body fat. In general, only fat and lean masses were impacted by participant and scan qualities (obesity category, sex, the magnitude of the body composition variables, and time between scans). We conclude that long-term precision error values are impacted by BMI, and sex. Our long-term precision error estimates may be more suitable than short-term precision for calculating least significant change and monitoring time intervals.

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