Multifractal Analysis for Nutritional Assessment

• Type of Material: Article
• Authors: Youngja Park, Emory University; Kichun Lee, Hanyang University; Thomas R Ziegler, Emory University; Greg Martin, Emory University; Gautam Hebbar, Emory University; Brani Vidakovic, Georgia Institute of Technology; Dean P Jones, Emory University
• Language: English
• Date: 2013
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2013 Park et al
• License: Creative Commons Attribution 3.0 Unported
• Final Published Version (URL): http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0069000
• Title of Journal or Parent Work: PLoS ONE
• ISSN: 1932-6203
• Volume: 8
• Issue: 8
• Start Page: e69000
• End Page: e69000
• Grant/Funding Information: This research was supported in part by National Institutes of Health grants ES012929, ES009047 and DK066008 (DPJ); DK55850 and K24 RR023356 (TRZ);; the Emory University General Clinical Research Center grant M01 RR00039/UL1 RR025008.; The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
• Research Categories: Health Sciences, General

Abstract

The concept of multifractality is currently used to describe self-similar and complex scaling properties observed in numerous biological signals. Fractals are geometric objects or dynamic variations which exhibit some degree of similarity (irregularity) to the original object in a wide range of scales. This approach determines irregularity of biologic signal as an indicator of adaptability, the capability to respond to unpredictable stress, and health. In the present work, we propose the application of multifractal analysis of wavelet-transformed proton nuclear magnetic resonance (1H NMR) spectra of plasma to determine nutritional insufficiency. For validation of this method on 1H NMR signal of human plasma, standard deviation from classical statistical approach and Hurst exponent (H), left slope and partition function from multifractal analysis were extracted from 1H NMR spectra to test whether multifractal indices could discriminate healthy subjects from unhealthy, intensive care unit patients. After validation, the multifractal approach was applied to spectra of plasma from a modified crossover study of sulfur amino acid insufficiency and tested for associations with blood lipids. The results showed that standard deviation and H, but not left slope, were significantly different for sulfur amino acid sufficiency and insufficiency. Quadratic discriminant analysis of H, left slope and the partition function showed 78% overall classification accuracy according to sulfur amino acid status. Triglycerides and apolipoprotein C3 were significantly correlated with a multifractal model containing H, left slope, and standard deviation, and cholesterol and high-sensitivity C-reactive protein were significantly correlated to H. In conclusion, multifractal analysis of 1H NMR spectra provides a new approach to characterize nutritional status.