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Role of Muscle Ultrasound for the Study of Frailty in Elderly Patients with Diabetes: A Pilot Study
Role of Muscle Ultrasound for the Study of Frailty in Elderly Patients with Diabetes: A Pilot Study
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Role of Muscle Ultrasound for the Study of Frailty in Elderly Patients with Diabetes: A Pilot Study
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Role of Muscle Ultrasound for the Study of Frailty in Elderly Patients with Diabetes: A Pilot Study
Role of Muscle Ultrasound for the Study of Frailty in Elderly Patients with Diabetes: A Pilot Study

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Role of Muscle Ultrasound for the Study of Frailty in Elderly Patients with Diabetes: A Pilot Study
Role of Muscle Ultrasound for the Study of Frailty in Elderly Patients with Diabetes: A Pilot Study
Journal Article

Role of Muscle Ultrasound for the Study of Frailty in Elderly Patients with Diabetes: A Pilot Study

2023
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Overview
Background: Sarcopenia and diabetes contribute to the development of frailty. Therefore, accessible methods, such as muscle ultrasounds (MUSs), to screen for sarcopenia should be implemented in clinical practice. Methods: We conducted a cross-sectional pilot study including 47 patients with diabetes (mean age: 77.72 ± 5.08 years, mean weight: 75.8 kg ± 15.89 kg, and body mass index: 31.19 ± 6.65 kg/m2) categorized as frail by the FRAIL Scale or Clinical Frailty Scale and confirmed by Fried’s Frailty Phenotype or Rockwood’s 36-item Frailty Index. We used the SARC-F questionnaire to identify sarcopenia. The Short Physical Performance Battery (SPPB) and the Timed Up and Go (TUG) tests were used to assess physical performance and the risk of falls, respectively. In addition, other variables were measured: fat-free mass (FFM) and Sarcopenia Risk Index (SRI) with the bioimpedance analysis (BIA); thigh muscle thickness (TMT) of the quadriceps with MUS; and hand-grip strength with dynamometry. Results: We observed correlations between the SARC-F and FFM (R = −0.4; p < 0.002) and hand-grip strength (R = −0.5; p < 0.0002), as well as between the TMT and FFM of the right leg (R = 0.4; p < 0.02) and the SRI (R = 0.6; p < 0.0001). We could predict sarcopenia using a logistic regression model with a ROC curve (AUC = 0.78) including FFM, handgrip strength, and TMT. The optimal cut-off point for maximum efficiency was 1.58 cm for TMT (sensitivity = 71.4% and specificity = 51.5%). However, we did not observe differences in the TMT among groups of greater/less frailty based on the SARC-F, SPPB, and TUG (p > 0.05). Conclusions: MUSs, which correlated with the BIA (R = 0.4; p < 0.02), complemented the diagnosis, identifying regional sarcopenia of the quadriceps in frail patients with diabetes and improving the ROC curve to AUC = 0.78. In addition, a TMT cut-off point for the diagnosis of sarcopenia of 1.58 cm was obtained. Larger studies to validate the MUS technique as a screening strategy are warranted.