Predicting Basal Metabolic Rate in Men with Motor Complete Spinal Cord Injury

This study aimed to assess the accuracy of existing basal metabolic rate (BMR) prediction equations in men with chronic (>1 yr) spinal cord injury (SCI). The primary aim is to develop new SCI population-specific BMR prediction models, based on anthropometric, body composition, and/or demographic variables that are strongly associated with BMR.

Thirty men with chronic SCI (paraplegic, n = 21, tetraplegic, n = 9) 35 ± 11 yr old (mean ± SD) participated in this cross-sectional study. Criterion BMR values were measured by indirect calorimetry. Body composition (dual-energy x-ray absorptiometry) and anthropometric measurements (circumferences and diameters) were also taken. Multiple linear regression analysis was performed to develop new SCI-specific BMR prediction models. Criterion BMR values were compared with values estimated from six existing and four developed prediction equations.

Existing equations that use information on stature, weight, and/or age significantly (P < 0.001) overpredicted measured BMR by a mean of 14%-17% (187-234 kcal·d). Equations that used fat-free mass (FFM) accurately predicted BMR. The development of new SCI-specific prediction models demonstrated that the addition of anthropometric variables (weight, height, and calf circumference) to FFM (model 3; r = 0.77), explained 8% more of the variance in BMR than FFM alone (model 1; r = 0.69). Using anthropometric variables, without FFM, explained less of the variance in BMR (model 4; r = 0.57). However, all the developed prediction models demonstrated acceptable mean absolute error ≤6%.

BMR can be more accurately estimated when dual-energy x-ray absorptiometry-derived FFM is incorporated into prediction equations. Using anthropometric measurements provides a promising alternative to improve the prediction of BMR, beyond that achieved by existing equations in persons with SCI.