Determinants of skeletal muscle catabolism after severe burn

Abstract

Objective: To determine which patient factors affect the degree of catabolism after severe burn.

Summary background data: Catabolism is associated with severe burn and leads to erosion of lean mass, impaired wound healing, and delayed rehabilitation.

Methods: From 1996 to 1999, 151 stable-isotope protein kinetic studies were performed in 102 pediatric and 21 adult subjects burned over 20-99. 5% of their total body surface area (TBSA). Patient demographics, burn characteristics, and hospital course variables were correlated with the net balance of skeletal muscle protein synthesis and breakdown across the leg. Data were analyzed sequentially and cumulatively through univariate and cross-sectional multiple regression.

Results: Increasing age, weight, and delay in definitive surgical treatment predict increased catabolism (P < .05). Body surface area burned increased catabolism until 40% TBSA was reached; catabolism did not consistently increase thereafter. Resting energy expenditure and sepsis were also strong predictors of net protein catabolism. Among factors that did not significantly correlate were burn type, pneumonia, wound contamination, and time after burn. From these results, the authors also infer that gross muscle mass correlates independently with protein wasting after burn.

Conclusions: Heavier, more muscular subjects, and subjects whose definitive surgical treatment is delayed are at the greatest risk for excess catabolism after burn. Sepsis and excessive hypermetabolism are also associated with protein catabolism.

Figure 1. Association between admission weight and negative protein balance. Data presented as mean ± SEM.

Figure 2. (A) Association between burn size and negative protein balance. Curve represents a “best fit” equation of authors’ data; best fit was a cubic expression as determined by the Akaike information criterion. R = 0.34. (B) Influence of burn size >40% total body surface area on catabolism. Data presented as mean ± SEM. *P = .0001 by Student t test.

Figure 3. Association between time to primary wound excision and negative protein net balance. Data presented as mean ± SEM.

Figure 4. Association between metabolic rate and negative net protein balance. Metabolic rate is expressed as the percentage of energy expenditure predicted by the Harris-Benedict equation (resting energy expenditure/basal metabolic rate). Data presented as mean ± SEM. All groups statistically different (P < .05) from each other by one-way ANOVA followed by Tukey correction for repeated measures.

Figure 5. (A) Influence of burn sepsis on hypermetabolism and catabolism. Hypermetabolism is expressed as the percentage of energy expenditure predicted by the Harris-Benedict equation. Data presented as mean ± SEM. *P < .0001 by Student t test. (B) Influence of sepsis, as defined by the American Association of Chest Physicians/Society of Critical Care Medicine, on hypermetabolism and catabolism. Hypermetabolism is expressed as the percentage of energy expenditure predicted by the Harris-Benedict equation. Data presented as mean ± SEM. *P < .0001 by Student t T-test.

Figure 6. Association between age and negative protein balance. Data presented as mean ± SEM.

Figure 7. Differences in catabolic response between genders. Data presented as mean ± SEM. *P = .04 by Student t test.