All ETDs from UAB

Advisory Committee Chair

Louis B Nabors

Advisory Committee Members

Marian A Brown

Robert A Oster

Betty E Darnell

Document Type


Date of Award


Degree Name by School

Master of Science (MS) School of Health Professions


Glioblastoma multiforme (GBM) is a rare brain tumor, yet accounts for 80% of malignant brain tumors and has a five-year survival rate of < 5%. Few studies have evaluated nutrition recommendations and outcomes of this disease, including caloric needs. The purpose of this study was to find the best predictive equation for resting energy expenditure (REE) for GBM patients and evaluate bioelectrical impedance analysis (BIA) as a clinical tool for estimating REE and fat free mass (FFM) of GBM patients. REE was measured with indirect calorimetry. FFM was measured with DXA and estimated with BIA. Published predictive equations for REE were calculated to compare to measured REE. Six equations used variables easily attained in a clinical setting and three used FFM. Correlation analysis was used to evaluate the strength of the relationships between measured and predicted REE. Agreement between methods on the group level was assessed by comparing the group means of measured and predicted REE with paired t-tests. The Bland-Altman approach was used to find agreement between the methods on the individual level. Analysis included fifteen newly diagnosed GBM patients (7 male and 8 female; mean age 57.1±11.6 years) to evaluate equations using clinical variables and a subsample of eight to evaluate predictive equations using FFM. All the predictive equations overestimated measured REE. The Mifflin-St Jeor was the only equation using clinical variables which was not significantly different from measured REE (p=0.054) and had the lowest bias (73 kcal/day) and narrowest limits of agreement. Likewise, Cunningham and Wang equations using FFM from DXA were not significantly different from measured (p=0.261 and p=0.072, respectively). BIA overestimated FFM compared to DXA, 54.1 kg and 49.2 kg, respectively (p<0.001). More visits with both DXA and BIA measurements available are needed before predictive equations with clinical variables and predictive equations with FFM can be compared. Due to the ease of obtaining clinical variables and the low bias and narrow limits of agreement found for the Mifflin equation, at this time it appears to be the best predictive equation for individuals with GBM.



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