Predicting Basal Metabolic Rate After Spinal Cord Injury

Abstract

PURPOSE: To assess the accuracy of existing basal metabolic rate (BMR) prediction equations in men with chronic (> 1 year) 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. METHODS: Thirty men with chronic SCI (Paraplegic; n = 21, Tetraplegic; n = 9), aged 35 ± 11 years (mean ± SD) participated in this cross-sectional study. Criterion BMR values were measured by indirect calorimetry. Body composition (dual energy X-ray absorptiometry; DXA) and anthropometric measurements (circumferences and diameters) were also taken. Criterion BMR values were compared to values estimated from six commonly used prediction equations. Multiple linear regression analysis was performed to develop new SCI-specific BMR prediction models. RESULTS: Existing equations that use information on stature, weight and/or age, significantly (P < 0.001) over-predicted measured BMR by a mean of 14-17% (187-234 kcal/day). Equations that utilised 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; r2 = 0.77), explained 8% more of the variance in BMR than FFM alone (Model 1; r2 = 0.69). Using anthropometric variables, without FFM, explained less of the variance in BMR (Model 4; r2 = 0.57). However, all the developed prediction models demonstrated acceptable mean absolute error ≤ 6%. CONCLUSIONS: BMR can be more accurately estimated when DXA derived FFM is incorporated into prediction equations. Utilising anthropometric measurements provides a promising alternative to improve the prediction of BMR, beyond that achieved by existing equations in persons with SCI.