Influence of Muscle Mass and Physical Activity on Serum and Urinary Creatinine and Serum Cystatin C Alessandra Cala ´bria Baxmann,* Marion Souza Ahmed, † Nata ´lia Cristina Marques,* Viviane Barcellos Menon,* Aparecido Bernardo Pereira, † Gianna Mastroianni Kirsztajn, † and Ita Pfeferman Heilberg † *Nutrition Program, Universidade Federal de Sa ˜o Paulo, and † Nephrology Division, Universidade Federal de Sa ˜o Paulo, Sa ˜o Paulo, Brazil Background and objectives: For addressing the influence of muscle mass on serum and urinary creatinine and serum cystatin C, body composition was assessed by skinfold thickness measurement and bioelectrical impedance analyses. Design, setting, participants, & measurements: A total of 170 healthy individuals (92 women, 78 men) were classified as sedentary or with mild or moderate/intense physical activity. Blood, 24-h urine samples, and 24-h food recall were obtained from all individuals. Results: Serum and urinary creatinine correlated significantly with body weight, but the level of correlation with lean mass was even greater. There was no significant correlation between body weight and lean mass with cystatin C. Individuals with moderate/intense physical activity presented significantly lower mean body mass index (23.1  2.5 versus 25.7  3.9 kg/m 2 ) and higher lean mass (55.3  10.0 versus 48.5  10.4%), serum creatinine (1.04  0.12 versus 0.95  0.17 mg/dl), urinary creatinine (1437  471 versus 1231  430 mg/24 h), protein intake (1.4  0.6 versus 1.1  0.6 g/kg per d), and meat intake (0.7  0.3 versus 0.5  0.4 g/kg per d) than the sedentary individuals. Conversely, mean serum cystatin did not differ between these two groups. A multivariate analysis of covariance showed that lean mass was significantly related to serum and urinary creatinine but not with cystatin, even after adjustment for protein/meat intake and physical activity. Conclusions: Cystatin C may represent a more adequate alternative to assess renal function in individuals with higher muscle mass when mild kidney impairment is suspected. Clin J Am Soc Nephrol 3: 348-354, 2008. doi: 10.2215/CJN.02870707 A ccurate renal function measurements are important in the diagnosis and treatment of kidney diseases, ad- justment of drug dosages, and decision-making re- garding when to initiate renal replacement therapy. Serum creatinine is the most commonly used indicator of renal func- tion, but its measurement suffers from a variety of analytical interferences and significant standardization problems (1,2). Serum creatinine can be affected by age, gender, ethnicity, dietary protein intake, and lean mass and may remain within the reference range despite marked renal impairment in pa- tients with low muscle mass. Consequently, the sensitivity of serum creatinine for the early detection of kidney disease is poor and not a good predictor when analyzing the elderly (3,4). Conversely, theoretically, serum creatinine may be falsely in- creased in individuals with higher muscle mass and normal renal function. The GFR represents the best overall assessment of kidney function, but the gold standard techniques for the measurement of GFR, such as inulin clearance, [ 125 I]iothalamate, 51 Cr-EDTA, 99m Tc-diethylenetriaminepentaacetic acid, and iohexol are too labor-intensive and costly for routine clinical use (5,6), so cre- atinine clearance is used instead. To rid the need of 24-h urine collections, several serum creatinine– based prediction formulas have been proposed to predict GFR (7–16). The equations of Cockcroft and Gault (7,8) and the one derived from the Modification of Diet in Renal Disease (MDRD) study (10) are the most widely ac- cepted; however, the competence of such formulas to predict GFR in patients with normal values of serum creatinine is debated. Despite the important influence of muscle mass on serum creatinine, the different equations used to predict GFR do not include parameters of body composition such as lean mass. Human body mass can be partitioned into two main compart- ments: Fat and lean (fat-free) mass. The latter comprises body cell mass (BCM), bone mass, and extracellular water. The gold standard techniques for the measurement of body composition include hydrodensitometry, computed tomography, magnetic resonance imaging, dual-photon absorptiometry, neutron acti- vation analysis, total body potassium counting, and isotope dilution (17,18). Nevertheless, in clinical practice, the indirect, low-cost, noninvasive methods of determining human body composition, such as bioelectrical impedance and skinfold thickness, are used instead (17). Muscle mass is extremely Received July 16, 2007. Accepted December 16, 2007. Published online ahead of print. Publication date available at www.cjasn.org. Correspondence: Dr. Ita Pfeferman Heilberg, Universidade Federal de Sa ˜o Paulo, Ne- phrology Division, Rua Botucatu 740, Vila Clementino, Sa ˜o Paulo, SP, 04023-900, Brazil. Phone: 55-11-5574-6300; Fax: 55-11-5573-9652; E-mail: ipheilberg@nefro.epm.br Copyright © 2008 by the American Society of Nephrology ISSN: 1555-9041/302–0348