Simplified method of clinical phenotyping for older men and women using established field-based measures David H. Fukuda a, ⁎, Abbie E. Smith-Ryan b , Kristina L. Kendall c , Jordan R. Moon d,e , Jeffrey R. Stout f a Institute of Exercise Physiology and Wellness, University of Central Florida, 12494 University Boulevard, Orlando, FL 32816, United States b Department of Exercise and Sport Science, University of North Carolina, Chapel-Hill, Chapel Hill, NC, United States c Department of Health and Kinesiology, Georgia Southern University, Statesboro, GA, United States d Sports Science Center Research Institute, MusclePharm, Inc., Denver, CO, United States e Department of Sports Exercise Science, United States Sports Academy, Daphne, AL, United States f Institute of Exercise Physiology and Wellness, University of Central Florida, Orlando, FL, United States abstract article info Article history: Received 21 August 2013 Received in revised form 2 October 2013 Accepted 8 October 2013 Available online 16 October 2013 Section Editor: Christiaan Leeuwenburgh Keywords: Body composition Sarcopenia Obesity Sarcopenic-obesity Phenotype Isoperformance The purpose of this investigation was to determine body composition classification using field-based testing measurements in healthy elderly men and women. The use of isoperformance curves is presented as a method for this determination. Baseline values from 107 healthy Caucasian men and women, over the age of 65 years old, who participated in a separate longitudinal study, were used for this investigation. Field-based measurements of age, height, weight, body mass index (BMI), and handgrip strength were recorded on an individual basis. Relative skeletal muscle index (RSMI) and body fat percentage (FAT%) were determined by dual-energy X-ray absorptiometry (DXA) for each participant. Sarcopenia cut-off values for RSMI of 7.26 kg·m -2 for men and 5.45 kg·m -2 for women and elderly obesity cut-off values for FAT% of 27% for men and 38% for women were used. Individuals above the RSMI cut-off and below the FAT% cut-off were classified in the normal phenotype category, while individuals below the RSMI cut-off and above the FAT% cut-off were classified in the sarcopenic- obese phenotype category. Prediction equations for RSMI and FAT% from sex, BMI, and handgrip strength values were developed using multiple regression analysis. The prediction equations were validated using double cross- validation. The final regression equation developed to predict FAT% from sex, BMI, and handgrip strength resulted in a strong relationship (adjusted R 2 = 0.741) to DXA values with a low standard error of the estimate (SEE = 3.994%). The final regression equation developed to predict RSMI from the field-based testing measures also resulted in a strong relationship (adjusted R 2 = 0.841) to DXA values with a low standard error of the estimate (SEE = 0.544 kg·m -2 ). Isoperformance curves were developed from the relationship between BMI and handgrip strength for men and women with the aforementioned clinical phenotype classification criteria. These visual representations were used to aid in the classification and evaluation of sarcopenia, obesity, and sarcopenic-obesity in elderly individuals. Future research should replicate the current findings with an increased sample size and the development of tailored interventions for each body composition category. © 2013 Elsevier Inc. All rights reserved. 1. Introduction After progressively increasing throughout the first four decades of life, skeletal muscle mass begins to slowly decrease, termed sarcopenia, and is linked with a concomitant increase in body fat percentage (FAT%)(Stenholm et al., 2008). Baumgartner termed these changes “syndromes of disordered body composition, ” or body composition phenotypes, and noted that the association between skeletal muscle mass and FAT% varied with age (Baumgartner, 2000). Despite lacking a consistent definition and etiology for the age-related variations in body composition, it is estimated that the loss of muscle mass in the elderly resulted in health care costs of over $18.4 billion in the United States (Janssen et al., 2004b), and that the number of people over the age of 60 years old affected worldwide could increase to 1.2 billion in the next two decades (Cruz-Jentoft et al., 2010). In response to the ongoing research and potential economic impact involving these multifactorial syndromes, a number of international working groups have been formed to critically define the disorders and to develop diagnostic criteria for clinical use (Cruz-Jentoft et al., 2010; Fielding et al., 2011). While these working groups are serving the clinical and Experimental Gerontology 48 (2013) 1479–1488 Abbreviations: BMI, body mass index; RSMI, relative skeletal muscle index; FAT%, body fat percentage; DXA, dual-energy X-ray absorptiometry; SD, standard deviation; SEE, standard error of estimate; TE, total error; CE, constant error. ⁎ Corresponding author. E-mail addresses: david.fukuda@ucf.edu (D.H. Fukuda), abbsmith@email.unc.edu (A.E. Smith-Ryan), kkendall@georgiasouthern.edu (K.L. Kendall), jordan@musclepharm.com (J.R. Moon), jeffrey.stout@ucf.edu (J.R. Stout). 0531-5565/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.exger.2013.10.005 Contents lists available at ScienceDirect Experimental Gerontology journal homepage: www.elsevier.com/locate/expgero