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

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.

Characteristics of sarcopenia subjects in arterial pulse spectrum analysis.

Aims: Sarcopenia is significantly associated with the number of cardiovascular and metabolic diseases, however, the underlying pathophysiological processes are largely unknown. This study performed harmonic index of finger photoplethysmography (PPG) waveforms with the aims of distinguishing different arterial pulse waveform signals between sarcopenia, presarcopenia, dynapenia, and healthy subjects.

Methods: Sixty-eight subjects were enrolled and obtained 1-min PPG signals, then were assigned to four age-matched groups: control, dynapenia, presarcopenia, and sarcopenia which definition according to Asian Working Group for Sarcopenia (AWGS): 2019 Consensus Update on Sarcopenia Diagnosis and Treatment.