Abstract
INTRODUCTION: Obesity and overweight are linked to metabolic disturbances, which contribute to the onset of diseases like type 2 diabetes (T2D) and cardiovascular disorders. Metabolic health is also closely linked to autonomic function, as measured by heart rate variability (HRV), making HRV a potential non-invasive indicator of metabolic status. While studies have examined metabolic changes with body mass index (BMI), the link between HRV and specific metabolic profiles in normal-weight (NW), overweight (OW), and obese (OB) individuals is less understood. Additionally, whether HRV can reliably predict key metabolites associated with metabolic dysregulation remains largely unexplored. METHODS: This study uses targeted metabolomics to profile amino acids and acylcarnitines in a group of academic employees across BMI categories (NW, OW, and OB) and investigates correlations between HRV variables and these metabolites. Finally, a machine learning approach was employed to predict relevant metabolite levels based on HRV features, aiming to validate HRV as a non-invasive predictor of metabolic health. RESULTS: NW, OW, and OB subjects showed different metabolic profiles, as demonstrated by sparse partial least square discriminant analysis (sPLS-DA). The main upregulated metabolites differentiating NW from OB were C6DC and C8:1, while C6DC and C10:2 were higher in OW than NW. Time- and frequency-domain HRV features show a good correlation with the regulated metabolites. Finally, our machine learning approach allowed us to predict the most regulated metabolites in OB and OW subjects using HRV metrics. CONCLUSION: Our study advances our understanding of the metabolic and autonomic changes associated with obesity and suggests that HRV could serve as a practical tool for non-invasively monitoring metabolic health, potentially facilitating early intervention in individuals with elevated BMI.