Assessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodology

Abstract

Metabolic diseases are one of the leading causes of death worldwide. Due to its lack of clinical manifestations for long periods, metabolic diseases are generally detected in advanced stages, when the risk of cardiovascular, ocular and renal complications is high. Thus, early detection of these disorders is essential to design effective health promotion strategies. Herein we provide a preliminary approach for the early diagnosis of metabolic diseases based on Principal Component Analysis (PCA) of autonomic features of sympathovagal Balance (SVB) to characterize the activity of the carotid bodies (CB). CBs are small chemoreceptors located in the bifurcation of the carotid arteries whose overactivation is intimately linked to early stages of metabolic disease through asymptomatic deregulation of the sympathetic nervous system. Herein we discuss parameters that can be extracted from these recordings using a PCA approach in response to two different challenge tests: 100% oxygen and administration of a mixed meal in healthy and type 2 diabetes volunteers. This methodology may represent a paradigm shift in the diagnosis of metabolic diseases through the characterization of CB activity, and aims to bridge the existing gap in early assessment of metabolic dysfunction.