Developing sarcopenia criteria and cutoffs for an older Caucasian cohort - a strictly biometrical approach.

backgroundThe aim of this strictly statistical approach was to provide a figure discrimination in a homogeneous cohort that is based on a main component, which includes disability, physical performance, and autonomy parameters.

methodsWe used data of 939 community-dwelling men aged ≥70 years, living in the area of Erlangen-Nürnberg, Germany. Briefly, we conducted a scaled principal component analysis based on criteria related to "physical function", "disability", "weakness", and "autonomy" to identify men who are likely to have sarcopenia as per the recognized sarcopenia criteria. Next, we applied fast-and-frugal decision trees, logistic regression, and classification and regression decision trees to classify men with and without sarcopenia, applying the 5% prevalence rate identified for this cohort by recent studies.

resultsIn summary, the best fast-and-frugal decision trees included gait velocity, handgrip strength, and two skeletal muscle mass indices (SMI) - appendicular skeletal muscle mass (ASMM)/body mass index (BMI) and ASMM/height

conclusionThe present approach that applied recognized sarcopenia criteria and was based on a predominately functional understanding of sarcopenia provided a simple and feasible decision rule for sarcopenia discrimination. In summary, we consider our approach as a strictly biometrical contribution within the development of sarcopenia screening methods. However, our tool needs to be further evaluated to validate its appropriateness to discriminate sarcopenia in this relevant cohort.