Affiliation:
1. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, Level 10 NUHS Tower Block, Singapore 119228
2. Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228
3. Department of Statistics and Applied Probability, Faculty of Science, National University of Singapore, Block S16, Level 6, 6 Science Drive 2, Singapore 117546
Abstract
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 P values, R2, 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 (P=0.0011), with the highest R2 (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.
Funder
Faculty Research Committee