It is widely accepted that malnutrition has an adverse impact on clinical outcomes. Exactly what defines clinical malnutrition is more contentious. The term malnutrition technically encompasses both over- and under-nutrition on both a macronutrient and micronutrient level, but is primarily employed clinically to identify individuals with some degree of protein-calorie undernutrition. This general understanding of clinical malnutrition has existed in western medicine for millennia, but the criteria used to characterize the condition have continued to change. One common feature that has persisted, however, is a change in body composition – particularly loss of skeletal muscle. Visual observation, anthropometric measures (e.g. measures of height and weight), and to a lesser extent assessment of dietary intake are common features of malnutrition diagnostic criteria intended to capture loss of body mass due to undernutrition. The utility of these measures, however, is limited by their subjectivity (visual observation and assessment of dietary intake) and lack of sensitivity (anthropometric measures). Incorporation of body composition technologies into nutrition assessment provides an avenue to improve nutrition assessment by providing increasingly sensitive and objective measures of body composition. This dissertation project explores the use of some of these technologies for lean tissue measurement in clinical populations at risk for malnutrition, ultimately demonstrating the clinical utility of skeletal muscle measures in predicting outcomes in an advanced heart failure population undergoing left ventricular assist device implantation. In the first study, the body composition technologies included were bioimpedance spectroscopy (BIS) and dual-energy x-ray absorptiometry (DXA). Raw bioimpedance parameters, phase angle (PA) and impedance ratio (IR), have been found to be prognostic in various clinical populations. This has led to the development of published cut-points by a number of research groups that account for age, body mass index (BMI), and gender. Because these cut-points were developed in homogenous populations, however, a large, ethnically diverse dataset (The National Health and Examination Survey; NHANES) was analyzed to determine if ethnicity influenced PA and IR measures. Ethnicity was found to influence PA and IR measures and suggested cut-points, based on DXA measured fat-free mass index, were created for use in clinical populations. In the second study, two different software programs (NIH ImageJ and Tomovision sliceOmatic) commonly used to generate measures of computed tomography (CT) derived skeletal muscle cross-sectional area (CSA) were compared. CT-derived measures of skeletal muscle CSA at the level of the third lumbar vertebrae (L3) were obtained in individuals with head and neck cancer and individuals with advanced heart failure. Both software programs were found to be in excellent agreement. A secondary objective was to determine the impact of a corrigendum that followed publication of an ImageJ tutorial. The ImageJ tutorial corrigendum was found to produce a clinically significant difference when interpreted against a published cut-point. In the third study, pre-operative CT-derived unilateral pectoralis muscle measures were obtained in individuals who had undergone left ventricular assist device (LVAD) implantation. Measures of both muscle quantity and attenuation were found to be novel and powerful predictors of mortality following LVAD implantation. Unilateral pectoralis muscle CSA, standardized for height (cm2/m2), was associated with a 27% reduction in hazard of death after LVAD. Each 5-unit increase in unilateral pectoralis muscle mean Hounsfield unit was associated with a 22% reduction in hazard of death after LVAD. Body composition, particularly lean tissue, is intimately tied to nutrition status in a clinical setting. Current nutrition assessment methods, however, rely on methods that lack objectivity. Clinically available body composition technologies, such as bioimpedance and CT, are capable of providing objective measures of lean tissue, which may enhance nutrition assessment. Results from the described studies support the clinical utility of these objective measures.
University of Minnesota Ph.D. dissertation. May 2018. Major: Nutrition. Advisor: Carrie Earthman. 1 computer file (PDF); xi, 113 pages.
Implications of lean tissue measurement and nutrition status on outcomes in clinical populations at risk for malnutrition.
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