Table of Contents
Advances in Nephrology
Volume 2014 (2014), Article ID 375614, 6 pages
Research Article

Comparison of Different Measures of Fat Mass and Their Association with Serum Cystatin C Levels

1Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, Level 10 NUHS Tower Block, Singapore 119228
2Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228
3Department of Statistics and Applied Probability, Faculty of Science, National University of Singapore, Block S16, Level 6, 6 Science Drive 2, Singapore 117546

Received 24 June 2014; Accepted 24 September 2014; Published 7 October 2014

Academic Editor: Carlos G. Musso

Copyright © 2014 Boon Wee Teo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Introduction. Cystatin C (CysC) is a glomerular filtration rate (GFR) marker affected by GFR and obesity. Because percentage body fat (%BF) distribution is affected by ethnicity, different measures of %BF may improve CysC prediction. This study aims to create multivariate models that predict serum CysC and determine which %BF metric gives the best prediction. Methods. Serum CysC was measured by nephelometric assay. We estimated %BF by considering weight, body mass index, waist-hip ratio, triceps skin fold, bioimpedance, and Deurenberg and Yap %BF equations. A base multivariate model for CysC was created with a %BF metric added in turn. The best model is considered by comparing values, , Akaike information criterion (AIC), and Bayesian information criterion (BIC). Results. There were 335 participants. Mean serum CysC and creatinine were 1.27 mg/L and 1.44 mg/dL, respectively. Variables for the base model were age, gender, ethnicity, creatinine, serum urea, c-reactive protein, log GFR, and serum albumin. %BF had a positive correlation with CysC. The best model for predicting CysC included bioimpedance-derived %BF (), with the highest (0.917) and the lowest AIC and BIC (−371, −323). Conclusion. Obesity is associated with CysC, and the best predictive model for CysC includes bioimpedance-derived %BF.