Frontiers in Endocrinology (Jul 2024)
New specific skeletal muscle mass index cut-offs for the assessment of sarcopenia in patients with severe obesity
Abstract
IntroductionBioelectrical impedance analysis (BIA) is the most used tool in clinical practice to evaluate body composition in patients with obesity. The skeletal muscle index (SMI) defined by BIA has been proposed for the identification of sarcopenia, but there are currently no univocal cutoffs for this condition. In this study, we aimed: 1) to determine the prevalence of sarcopenia in patients with severe obesity using the current cutoffs of SMI; 2) to define new specific cutoffs; 3) to validate the new cutoffs; and 4) to re-determine the prevalence of sarcopenia.MethodsA total of 300 patients, 74% women and 26% men (mean age = 42.6 ±; 9 years), with morbid obesity (mean BMI = 46.7 ±; 6.5 kg/m2) followed by the Unit of Endocrinology from January 2014 to December 2020 were retrospectively evaluated. SMI was calculated as the skeletal muscle mass normalized for squared height through the BIA equation by Janssen et al.ResultsThe prevalence of sarcopenic obesity calculated using the cutoff points reported by De Rosa et al. (7.3 kg/h2 for women and 9.5 kg/h2 for men) was 2.3%. The prevalence of sarcopenia was calculated using the new cutoffs: with the cutoff obtained from the standard deviation method (8.2 kg/h2 for women and 10.2 kg/h2 for men), a prevalence of 14.7% was observed, whereas the prevalence reached 47.6% when using the cutoff calculated through the K-means unsupervised cluster (9.2 kg/h2 for women and 11.3 kg/h2 for men). The new cutoffs were validated with a second sample consisting of 300 patients with morbid obesity (BMI = 44.9 ±; 6.7 kg/m2): the rate of sarcopenic patients was still higher than that observed in the training cohort (56%). After the matching procedure (by BMI and age), the rates of sarcopenic patients were similar in both groups (50.2% in the validation group and 53% in the training group, p = 0.6).ConclusionThe new cutoffs calculated with cluster analysis could better identify sarcopenia in morbidly obese patients. However, further studies are needed to validate these cutoffs in different patient cohorts.
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