De novo Explorations of Sarcopenia via a Dynamic Model

Possible-sarcopenic screening with disturbed plasma amino acid profile in the elderly

BACKGROUND

The mass and strength of skeletal muscle decline with age, leading to its progressive dysfunction. High-throughput metabolite profiling provides the opportunity to reveal metabolic mechanisms and the identification of biomarkers. However, the role of amino acid metabolism in possible sarcopenia remains unclear.

OBJECTIVES

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.

METHODS

A cross-sectional, correlational research design was used for this study. Thirty possible-sarcopenic elderly participants were recruited (n = 30), as determined by the Asian Working Group for Sarcopenia (AWGS). 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. Both groups were conducted the body composition analysis, physical function examination, and plasma amino acid-targeted metabolomics. The amino acids in plasma were measured using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS-MS). Also, orthogonal partial least-squares-discriminant analysis (OPLS-DA) was applied to characterize the plasma amino acid profile.

RESULTS

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. 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. According to the multi-analysis, glutamine, serine, lysine, threonine, and proline were determined as the potential markers that indicated possible sarcopenia.

CONCLUSIONS

The findings characterize significantly altered plasma amino acid metabolisms in the elderly with possible sarcopenia, which aids to screening people who are at a high risk of developing condition, and motivating to design new preventive and therapeutic approaches.

Estimation of Heart Rate Using Regression Models and Artificial Neural Network in Middle-Aged Adults

Purpose: Heart rate is the most commonly used indicator in clinical medicine to assess the functionality of the cardiovascular system. Most studies have focused on age-based equations to estimate the maximal heart rate, neglecting multiple factors that affect the accuracy of the prediction. Methods: We studied 121 middle-aged adults at an average age of 57.2years with an average body mass index (BMI) of 25.9. The participants performed on a power bike with a starting wattage of 0W that was increased by 25W every 3min until the experiment terminated. Ambulatory blood pressure and electrocardiography were monitored through gas metabolic analyzers for safety concerns. Six descriptive characteristics of participants were observed, which were further analyzed using a multivariate regression model and an artificial neural network (ANN). Results: The input variables for the multivariate regression model and ANN were selected by correlation for the reduction of dimension. The accuracy of estimation by multivariate regression model and ANN was 9.74 and 9.42%, respectively, which outperformed the traditional age-based model (with an accuracy of 10.31%). Conclusion: This study provides comprehensive approaches to estimate the maximal heart rate using multiple indicators, revealing that both the multivariate regression model and ANN incorporated with age, resting heart rate (RHR), and second-order heart rate (SOHR) are more accurate than univariate models.