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.
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Specificity of the combined PTR-MS and machine learning model.
Timeframe: Through study completion, after all participant samples are collected and the final model is validated (anticipated within 1 year).
Sensitivity of the combined PTR-MS and machine learning model.
Timeframe: Through study completion, after all participant samples are collected and the final model is validated (anticipated within 1 year).
Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of the combined PTR-MS and machine learning model.
Timeframe: Through study completion, after all participant samples are collected and the final model is validated (anticipated within 1 year).