Novel Methods to Monitor Nutrition Status and Determine Protein Needs in Clinical Populations

Loading...
Thumbnail Image

Persistent link to this item

Statistics
View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Novel Methods to Monitor Nutrition Status and Determine Protein Needs in Clinical Populations

Published Date

2017-07

Publisher

Type

Thesis or Dissertation

Abstract

Malnutrition and muscle loss in hospitalized patients is a significant problem. The ability to accurately identify the development of malnutrition and monitor changes in muscle at the bedside are essential to optimizing nutrition interventions throughout treatment course and hospitalization; however, there is currently a lack of valid bedside tools. Furthermore, the provision of adequate protein and amino acids is crucial for maintaining muscle and optimizing outcomes in clinical settings. Yet, the methodologies used to determine protein needs have significant limitations, and current recommendations for dietary protein intake in clinical populations are not supported by strong evidence. This dissertation project consists of a series of studies that explore novel approaches for evaluating lean tissue and muscle (as core components of nutritional status) at the bedside, and determining protein requirements in the clinical setting. In the first study, using a large and ethnically-diverse healthy population sample (NHANES 1999-2004), it was determined that ethnicity significantly influences the values of phase angle (PA) and impedance ratio (IR), two bioimpedance parameters currently being investigated as clinical markers. Based on the findings from this study, cut-points for PA and IR corresponding to low muscle mass defined by dual-energy X-ray absorptiometry (DXA) were established that can potentially serve as reference data for future clinical studies investigating the applications of PA and IR as markers of lean tissue and/or nutritional status. In the second study, it was determined that PA and IR could be used to assess low muscularity and predict clinical outcomes in a large sample of critically ill patients. PA and IR were moderately associated with muscle cross-sectional area (CSA) as determined by computed tomography (CT). Furthermore, PA and IR appeared to predict low CT-derived muscle CSA. In summary, PA and IR show promise in being able to aid in the identification of low muscularity and poor nutritional status in the ICU setting. In the third and ongoing study, a novel stable amino acid isotope multi-step feeding protocol is being employed to determine the protein intake required to prevent net protein loss (anabolic threshold) and to evaluate the relationship between protein intake and net protein synthesis (anabolic capacity) of individuals with head and neck cancer (HNC), following chemoradiation therapy. Moreover, a force-measuring ultrasound (US) device is being used to assess changes in muscle quantity and quality due to chemoradiation. There is a vital need to develop objective bedside methods capable of assessing muscle mass in hospitalized patients. Results from the described studies show the potential utility of bioimpedance and US to characterize changes in muscle mass in various clinical populations. Furthermore, as clinicians become better equipped at detecting changes in muscle, appropriate nutritional intervention is needed to stave off the loss of muscle. Amino acid tracer methods that can estimate whole body protein synthesis and breakdown are fundamental to the determination of more accurate protein and amino acid recommendations in clinical populations in order to improve patient outcomes.

Description

University of Minnesota Ph.D. dissertation. July 2017. Major: Nutrition. Advisor: Carrie Earthman. 1 computer file (PDF); xii, 123 pages.

Related to

Replaces

License

Collections

Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Kuchnia, Adam. (2017). Novel Methods to Monitor Nutrition Status and Determine Protein Needs in Clinical Populations. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/198383.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.