Simple SummaryIt is well known that diabetes and sarcopenia are risk factors for developing frailty. The latest consensus on the management of older people with diabetes recommends routine frailty screening in check-up consultations for these patients. Therefore, there is a need to find more precise imaging techniques to determine sarcopenia and to distinguish site-specific sarcopenia—in this particular case, the quadriceps—which allows deambulation. Ultrasound technology is a low-cost, fast, and accessible technique in consultations to study body composition in patients with frailty. In this study, our aim was to validate muscle ultrasounds to complement physical fragility diagnoses using specific tests, such as the SARC-F, bioelectrical impedance analysis (BIA), and dynamometer. In fact, we demonstrated that muscle ultrasounds correlated with the BIA and could, therefore, be a useful tool for identifying regional sarcopenia of the quadriceps in elderly patients with diabetes. As this was a pilot study, larger studies to validate this simple and affordable screening method are necessary.AbstractBackground: 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.