Association of subjective global assessment and adductor pollicis muscle thickness with the sarcopenia in older patients with type 2 diabetes

Abstract

Background and Aim: Sarcopenia is prevalent in older patients and increases the risk for negative outcomes during hospitalization and after hospital discharge. In patients with type 2 diabetes (T2D) this association may be even worse. Upon hospital admission, it is often difficult to identify sarcopenia, so the objective of this study was to assess whether the subjective global assessment (SGA), the European Society for Clinical Nutrition and Metabolism (ESPEN) and Global Leadership Initiative on Malnutrition (GLIM) criteria and/or usual anthropometric measures can predict sarcopenia. A secondary objective, to evaluate the accuracy of variables in the prediction of sarcopenia. Methodology: Patients 60 years old and with T2D were included. Malnutrition was evaluated in accordance with the guidelines of ESPEN and GLIM, and SGA. Anthropometric measurements were performed by Mid-arm circumference (MAC), mid-upper arm muscle circumference (MUAMC), and adductor pollicis muscle thickness (APMT) was performed. The sarcopenia was evaluated by handgrip strength, timed Up and Go (TUG) test and muscle mass by measuring the calf circumference (CC). Logistic regression was performed to assess the association of variables with Sarcopenia. Results: A total of 311 patients were included. The prevalence of malnutrition in accordance to ESPEN, GLIM and SGA was 18 (5.8%), 65 (21%) and 15 (4%), respectively. The MAC and MUAMC showed a negative relationship with sarcopenia (HR: 0.92 CI95% 0.85-0.99). However, patients with overweight had a 66% reduction in the risk of sarcopenia (HR: 0.34 CI95% 0.19-0.59). After adjustments, malnourished patients according to the SGA had a risk of HR: 5.65 (CI95% 1.64-19.38) of sarcopenia, similarly to patients with APMT <5 th HR: 2.81 (CI95% 1.53-5.13), ESPEN and GLIM criteria presented HR:3.10 (CI95%1.12-8.22) and HR:2.94 (CI95%1.64-5.27), respectively. The interaction between SGA and APMT after adjusting the model has been significant (HR: 7.23 CI95% 2.98-17.67). In the area under the curve (ROC), only SGA and APMT showed greater accuracy in the prediction of sarcopenia (AUC: 0.713 CI95% 0.650-0.803). Conclusion: In our sample, it was possible to predict sarcopenia through the malnutrition criteria of ESPEN and GLIM, SGA, MAC and APMT. Measures such as APMT associated with the SGA tool seem to better predict sarcopenia in older patients with T2D.