Simplified method of clinical phenotyping for older men and women using established field-based measures

Abstract

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 65years 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 7.26kg·m−2 for men and 5.45kg·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 R2=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 R2=0.841) to DXA values with a low standard error of the estimate (SEE=0.544kg·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.