Development of Novel Methods to Define Deficits in Appendicular Lean Mass Relative to Fat Mass

Background Recent studies suggest that adjustment of measures of lean mass for adiposity improves associations with physical function. Our objective was to develop and test a method to adjust appendicular lean mass for adiposity. Methods Whole-body DXA data in 14,850 adults in the National Health and Nutrition Examination Survey were used to generate sex-, and race-specific standard deviation scores (Z-Scores relative to age and T-scores relative to 25 year-olds) for appendicular lean mass index (ALMI, kg/m(2)) and fat mass index (FMI, kg/m(2)). Correlations between ALMI and FMI Z- and T-Scores were assessed within demographic categories. Fat-adjusted ALMI (ALMI(FMI)) scores were determined using residual methods. Sarcopenia was defined as a T-Score <-2.0 and low lean for age as a Z- Score <-1.0. Correlations with physical function were assessed in an at-risk population. Results Positive associations between ALMI and FMI Z- and T-Scores were significant (R >0.50; p<0.001) within all demographic categories. The impact of a unit greater FMI Z- score on ALMI Z- score was less in the elderly, men, white subjects, and among individuals with lower FMI (all tests for interaction p<0.001). There was fair agreement between ALMI and ALMIFMI estimates of sarcopenia and low lean for age [ Kappa: 0.46, 0.52, respectively (p<0.0001)]. Elderly subjects were likely to be re-classified as sarcopenic while young subjects were likely to be re-classified as normal using ALMIFMI. ALMIFMI T-scores resulted in approximately twice the number of subjects defined as sarcopenic, compared with ALMI T-Scores. (1299 v. 534). Among rheumatoid arthritis patients, ALMIFMI Z- scores correlated with physical function (Health Assessment Questionnaire: rho = -0.22, p = 0.04; Short Physical Performance Battery: rho = 0.27, p = 0.01); however, the ALMI Z-Score did not. Conclusions Adjustment of ALMI for the confounding association with FMI impacts the definition of lean mass deficits. These methods provide a practical tool for investigators and clinicians based on population-based reference data.