Assessment of tools for the detection of sarcopenia in older adults

The main objective of this study is to evaluate the best available and validated tools that reflect muscle state and function, in order to determine the presence of sarcopenia in older adults.

Thirty older adults (age ≥ 60 years) and 22 physically active young adults were recruited as a control group. Ultrasound was used to assess muscle morphology by measuring pennation angle and muscle thickness in the medial gastrocnemius and anterior forearm of the dominant limb (Kuyumcu, 2016). In a subset, magnetic resonance imaging (MRI) was used to quantify the psoas and thigh muscle areas. Grip strength and fatigue of the wrist flexor muscles were measured using a hand dynamometer and surface electromyography, while plantar flexor strength was assessed with the Heel Rise Test. Participants completed self-report questionnaires to assess physical activity (IPAQ), health status (SF-36), and suspicion of sarcopenia (Sarc-F). Binary logistic regression was performed to identify detection factors for sarcopenia, and ROC curve analysis was used to evaluate the accuracy of the model.

The results showed that older adults have lower electromyographic activity, muscle mass, and muscle strength compared to younger individuals (p0.05). Significant sex differences were also found, with men showing higher values in most of these variables. The binary logistic regression model indicated that lower functionality, greater pain (SF-36), and lower fatigability are detection factors of sarcopenia. The ROC curve analysis for the model (including significant detection variables such as functionality, pain, and fatigability) showed excellent detection capacity for sarcopenia (AUC= 0.96, S=67%, E=95%).

In addition to the quantification of muscle mass and strength, other factors such as functionality, pain, and fatigability can detect sarcopenia. However, it is necessary to evaluate the detection capacity model in more representative samples of the study population.

This study involved two groups with extreme age ranges to demonstrate the model’s detection capacity. Future research should focus on applying the model in more homogeneous groups. Moreover, exploring the model's ability to detect sarcopenia using these variables in comparison to magnetic resonance imaging, the gold standard for muscle mass quantification, is recommended.