Developing sarcopenia criteria and cutoffs for an older Caucasian cohort – a strictly biometrical approach
Received 11 March 2018
Accepted for publication 9 May 2018
Published 2 August 2018 Volume 2018:13 Pages 1365—1373
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Dr Richard Walker
Wolfgang Kemmler,1 Simon von Stengel,1 Matthias Kohl2
1Institute of Medical Physics, University of Erlangen-Nürnberg, Erlangen, Germany; 2Department of Medical and Life Sciences, University of Furtwangen, Villingen-Schwenningen, Germany
Background: The 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.
Methods: We 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.
Results: In 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/height2. Briefly, men below the cutoff point of 1.012 m/s for gait velocity were directly classified as sarcopenic. Faster men with a handgrip strength of >34.5 kg were excluded from further screening, while their weaker peers were assessed for SMI. Firstly, an ASMM/BMI-based exclusion criterion of >0.886 indicates no sarcopenia; while in men with a lower BMI-based SMI, an ASMM/height2 of <7.25 kg/m2 indicates sarcopenia. Of importance, about 72% of the participants can be classified without an SMI assessment.
Conclusion: The 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.
Keywords: sarcopenia screening, sarcopenia cutoff points, classification and regression tree, fast-and-frugal decision trees, Caucasian men aged 70+
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