Leveraging the Metabolic Fingerprint of Sleep Deprivation and Sleep Restriction for Forensic Applications: A Machine Learning Study in Oral Fluid Metabolomics

= 0.90) when using only 12 molecular features. This fingerprint was more pronounced in samples collected in the morning/midday hours. Nevertheless, at all time points, the overall correct predictions by far outweighed the incorrect ones. Four nights of sleep restriction did not lead to exploitable metabolic changes. This study presents a metabolic fingerprint of acute sleep deprivation in oral fluid under realistic conditions and explores practical implications and limitations of its machine learning-aided classification. Metabolomics-based, reference-free sleep loss detection holds potential for applications in forensic, clinical, and occupational contexts.