Blood Metabolome Shows Signatures of Metabolic Dysregulation in Obese and Overweight Subjects that Can be Predicted by Machine Learning Applied to Heart Rate Variability

Andrea Di Credico^1,2,3David Perpetuini⁴Pascal Izzicupo¹Giulia Gaggi^1,2,3Claudia Rossi^2,5Arcangelo Merla⁴Barbara Ghinassi^2,3,6Angela Di Baldassarre^1,2,3*Ines Bucci^1,2

• ¹Department of Medicine and Sciences of Aging, G. d'Annunzio University of Chieti and Pescara, Chieti, Italy
• ²Center of Advanced Studies and Technologies (CAST), Chieti, Italy
• ³UdA-Tech Lab, Chieti, Italy
• ⁴Department of Engineering and Geology, G. d'Annunzio University of Chieti and Pescara, Pescara, Italy
• ⁵Department of Science, G. d’Annunzio University of Chieti and Pescara, Chieti, Italy
• ⁶Department of Innovative Technologies in Medicine and Dentistry, University of Studies G. d'Annunzio Chieti and Pescara, Chieti, Italy

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 intertwined with 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 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. 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. NW, OW and OB subjects showed different metabolic profiles as demonstrated by sPLS-DA. The main upregulated metabolites differentiating NW from OB were C6DC and C8:1, while C6DC and C10:2 were higher in OW compared to NW. Time-and frequency domain HRV features show good correlation with the regulated metabolites. Finally, our machine learning approach allowed to predict the most regulated metabolites in OB and OW subject using HRV metrics. Our study advances 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.