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According to the multi-analysis, glutamine, serine, lysine, threonine, and proline were determined as the potential markers that indicated possible sarcopenia. The mean plasma concentrations of arginine, asparagine, phenylalanine, serine, lysine, glutamine, and threonine were significantly lower in the possible sarcopenia group, whereas cirulline, proline, serine, and glutamic acid concentrations were higher.

With respect to Handgrip Strength (HGS), the Five-Repetition Chair Stand Test (CS-5), the Six-Minute Walking Test (6MWT), the arm curl, the 30 s-Chair Stand Test (CST), the 2-Minute Step Test (2MST), the Timed Up-and-Go Test (TUGT), there was a decline in skeletal muscle function in the possible-sarcopenic group compared to the non-sarcopenic group. Also, orthogonal partial least-squares-discriminant analysis (OPLS-DA) was applied to characterize the plasma amino acid profile. The amino acids in plasma were measured using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS–MS). Both groups were conducted the body composition analysis, physical function examination, and plasma amino acid-targeted metabolomics. Meanwhile, a reference group of non-sarcopenic (sex-, age-, and Appendicular Skeletal muscle Mass Index (ASMI)-matched non-sarcopenic controls, n = 36) individuals was included to compare the potential differences in metabolic fingerprint of the plasma amino acids associated with sarcopenia. Thirty possible-sarcopenic elderly participants were recruited ( n = 30), as determined by the Asian Working Group for Sarcopenia (AWGS).

MethodsĪ cross-sectional, correlational research design was used for this study. The aim of this study included exploring variations in plasma amino acid concentrations in elderly individuals who have possible sarcopenia and further attempting to characterize a distinctive plasma amino acid profile through targeted metabolomics. However, the role of amino acid metabolism in possible sarcopenia remains unclear. High-throughput metabolite profiling provides the opportunity to reveal metabolic mechanisms and the identification of biomarkers. The mass and strength of skeletal muscle decline with age, leading to its progressive dysfunction.

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