Organ-tissue mass measurement allows modeling of REE and metabolically active tissue mass

Prediction of basal metabolism from organ size in the rat: relationship to strain, feeding, age, and obesity

Use of the weight of various organs and tissues together with their specific metabolic activity for prediction of basal metabolism (BM) seems to be promising. In this study we compared the use of this method with those based on simple or multiple regression analyses. We observed that 97.4% of differences in BM in a group of nine adult male Wistar rats weighing 273–517 g could be accounted for by changes in tissue and organ weights. BM measured in lean Zucker and Sprague-Dawley rats did not diverge from the prediction of the model by >1.6%. According to the organ-based model as well as multiple regression analyses, but not simple regression analyses, BM was increased 18–21% in young rats, decreased 6–7% in food restricted/refed rats, and decreased 19–21% in aged rats. Only with obese rats did the predictions of the two methods diverge. The main reason for this discrepancy seems to be the way adipose tissue size and metabolism are taken into account.

Lower limb skeletal muscle mass: development of dual-energy X-ray absorptiometry prediction model

Although magnetic resonance imaging (MRI) can accurately measure lower limb skeletal muscle (SM) mass, this method is complex and costly. A potential practical alternative is to estimate lower limb SM with dual-energy X-ray absorptiometry (DXA). The aim of the present study was to develop and validate DXA-SM prediction equations. Identical landmarks (i.e., inferior border of the ischial tuberosity) were selected for separating lower limb from trunk. Lower limb SM was measured by MRI, and lower limb fat-free soft tissue was measured by DXA. A total of 207 adults (104 men and 103 women) were evaluated [age 43 +/- 16 (SD) yr, body mass index (BMI) 24.6 +/- 3.7 kg/m(2)]. Strong correlations were observed between lower limb SM and lower limb fat-free soft tissue (R(2) = 0.89, P < 0.001); age and BMI were small but significant SM predictor variables. In the cross-validation sample, the differences between MRI-measured and DXA-predicted SM mass were small (-0.006 +/- 1.07 and -0.016 +/- 1.05 kg) for two different proposed prediction equations, one with fat-free soft tissue and the other with added age and BMI as predictor variables. DXA-measured lower limb fat-free soft tissue, along with other easily acquired measures, can be used to reliably predict lower limb skeletal muscle mass.

Resting energy expenditure-fat-free mass relationship: new insights provided by body composition modeling

The relationship between resting energy expenditure (REE) and metabolically active fat-free mass (FFM) is a cornerstone in the study of physiological aspects of body weight regulation and human energy requirements. Important questions, however, remain unanswered regarding the observed linear REE-FFM association in adult humans. This led us to develop a series of REE-body composition models that provide insights into the widely used simple linear REE-FFM prediction model derived experimentally in adult humans. The new models suggest that the REE-FFM relationship in mammals as a whole is curvilinear, that a segment of this function within a FFM range characteristic of adult humans can be fit with a linear equation almost identical to that observed from a composite review of earlier human studies, and that mammals as a whole exhibit a decrease in the proportion of FFM as high metabolic rate organs with greater FFM. The present study thus provides a new approach for examining REE-FFM relationships, advances in a quantitative manner previously observed albeit incompletely formulated REE-body composition associations, and identifies areas in need of additional research.

Metabolically active components of fat free mass and resting energy expenditure in nonobese adults

Resting energy expenditure (REE) and components of fat-free mass (FFM) were assessed in 26 healthy nonobese adults (13 males, 13 females). Detailed body composition analyses were performed by the combined use of dual-energy X-ray absorptiometry (DEXA), magnetic resonance imaging (MRI), bioelectrical impedance analysis (BIA), and anthropometrics. We found close correlations between REE and FFM(BIA) (r = 0.92), muscle mass(DEXA) (r = 0.89), and sum of internal organs(MRI) (r = 0.90). In a multiple stepwise regression analysis, FFM(BIA) alone explained 85% of the variance in REE (standard error of the estimate 423 kJ/day). Including the sum of internal organs(MRI) into the model increased the r(2) to 0.89 with a standard error of 381 kJ/day. With respect to individual organs, only skeletal muscle(DEXA) and liver mass(MRI) significantly contributed to REE. Prediction of REE based on 1) individual organ masses and 2) a constant metabolic rate per kilogram organ mass was very close to the measured REE, with a mean prediction error of 96 kJ/day. The very close agreement between measured and predicted REE argues against significant variations in specific REEs of individual organs. In conclusion, the mass of internal organs contributes significantly to the variance in REE.