Browsing by Subject "Malnutrition"

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    Implications of lean tissue measurement and nutrition status on outcomes in clinical populations at risk for malnutrition
    (2018-05) Teigen, Levi
    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.
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    Nutritional Status, Body Composition, and Psychosocial Outcomes Among Individuals with Advanced Head and Neck Cancers: A Prospective Investigation in An Outpatient Setting
    (2015-08) Mulasi, Urvashi
    Malnutrition among individuals with head and neck cancer (HNC) is of particular concern, with up to 40% - 57% with a compromised nutritional status even before beginning their treatment. Within the US, the prevalence of malnutrition has not been well-documented due to a lack of consensus on its definition and diagnosing markers. Therefore, the primary aim of this prospective natural history pilot study was to estimate the prevalence of malnutrition among individuals with HNC (n = 19) during and up to 3 months after treatment using the new Consensus malnutrition definition. The scored Patient-Generated Subjective Global Assessment (PG-SGA) was used as the reference standard to evaluate the sensitivity and specificity of the Consensus framework in defining malnutrition. Another aim of this research was to investigate the utility of raw bioimpedance parameters such as 50 kHz phase angle (PA) and 200 kHz/5 kHz impedance ratio (IR) to identify individuals with malnutrition, and to evaluate how bioimpedance markers relate to functional status outcomes. Finally, this research also assessed how malnutrition relates to quality of life (QoL) and self-efficacy perceptions among individuals with HNC. Results indicate that individuals with HNC are malnourished even before treatment initiation. Using the Consensus framework, 67% of our participants were malnourished before treatment; and the prevalence of malnutrition consistently increased during treatment and the post-treatment period. When compared to our reference standard PG-SGA, the Consensus criteria identified malnutrition with overall good sensitivity (95%) and specificity (43%). Bioimpedance markers PA and IR were useful in identifying individuals who were at increased risk for malnutrition and/or impaired functional status. From a psychosocial perspective, compared with well-nourished participants, malnourished individuals scored significantly lower in the global QoL and cognitive function scales and significantly higher in the disease- and treatment-related symptom scales and items. In the future, if clinicians are trained to assess malnutrition diagnostic markers before individuals with cancer undergo aggressive treatments, nutritional interventions could be initiated at an earlier time and loss in weight and/or lean tissue can be prevented. Early detection of malnutrition could also help with patient-specific intervention strategies aimed to improve overall health-related QoL outcomes.