Breath Mass Spectrometry in Metabolic Syndrome and Metabolically Healthy Obesity

This study aims to develop a non-invasive diagnostic method for metabolic syndrome (MetS) and metabolically healthy obesity (MHO) through analysis of exhaled air. Using proton-transfer-reaction mass spectrometry combined with machine learning algorithms, we will characterize volatile organic compound profiles in 300 participants across three groups: MetS patients, MHO patients, and healthy controls. The primary goal is to create and validate a classification model capable of accurately differentiating these metabolic states based on breath analysis.

This study focuses on characterizing the volatilome - the complete set of volatile organic compounds in exhaled air - as a novel biomarker source for metabolic health assessment. The study represents the first comprehensive attempt to compare volatilome signatures between metabolically healthy and unhealthy obesity phenotypes. Successful validation of this approach could establish breath analysis as a new diagnostic paradigm in metabolic medicine, enabling rapid, non-invasive screening and personalized treatment strategies for patients with obesity-related conditions. Methodological innovations include real-time breath analysis capabilities and development of specialized machine learning algorithms for pattern recognition in complex mass spectrometry data. The findings are expected to contribute significantly to understanding metabolic pathway alterations in different obesity phenotypes.