Discrimination between possible sarcopenia and metabolic syndrome using the arterial pulse spectrum and machine-learning analysis

Abstract

Sarcopenia is defined as decreased skeletal muscle mass and function, and is an important cause of frailty in the elderly, also being associated with vascular lesions and poor microcirculation. The present study aimed to combine noninvasive pulse measurements, frequency-domain analysis, and machine learning (ML) analysis (1) to determine the effects on the pulse waveform induced by sarcopenia and (2) to develop discriminating models for patients with possible sarcopenia. Radial blood pressure waveform (BPW) signals were measured noninvasively for 1 min in 133 subjects who visited Tri-Service General Hospital for geriatric health checkups. They were assigned to a robust group and a possible-sarcopenia group that combined dynapenia, presarcopenia, and sarcopenia. Two classification methods were used: ML analysis and a self-developed scoring system that used 40 harmonic pulse indices as features: amplitude proportions and their coefficients of variation, and phase angles and their standard deviations. Significant differences were found in several spectral indices of the BPW between possible-sarcopenia and robust subjects. Threefold cross-validation results indicated excellent discrimination performance, with AUC equaling 0.77 when using LDA and 0.83 when using our scoring system. The present noninvasive and easy-to-use measurement and analysis method for detecting sarcopenia-induced changes in the arterial pulse transmission condition could aid the discrimination of possible sarcopenia.