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.