[Breath metabolomic characteristics of schizophrenia and their potential for auxiliary diagnosis].

objectivesThe clinical diagnosis of schizophrenia (SCZ) primarily relies on psychiatrists' comprehensive judgment based on symptom assessment, medical history collection, and mental status examination. However, in the early stages of the disease, in cases with atypical symptom presentations, or when differentiation from other psychiatric disorders is required, diagnosis may be influenced by patients' subjective reporting, symptom fluctuations, and differences in evaluators' clinical experience. Detection of exhaled volatile organic compounds (VOCs) is a noninvasive, rapid, and repeatable approach that can provide metabolic information related to physiological and pathological states. This study aims to compare the characteristics of exhaled VOCs between patients with SCZ and healthy controls (HCs) using a high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) platform combined with machine learning methods, and to evaluate their discriminative performance for the auxiliary diagnosis of SCZ.

methodsAn exploratory case-control study was conducted, including 34 patients with SCZ and 34 HCs. All patients with SCZ were diagnosed independently by 2 licensed psychiatrists according to the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5). During study design, participants in the 2 groups were matched by sex, age, and body mass index (BMI). Exhaled breath samples were collected from all participants within 30 minutes after waking in the morning. Participants consumed a light diet on the evening before sampling and avoided foods with strong odors or irritant properties. Fasting for at least 8 hours before sampling was required, and participants were instructed not to brush their teeth on the sampling day but only to rinse their mouths with purified water. Healthy controls stayed for at least 8 hours in the same ward environment as patients with SCZ on the night before sampling to minimize the impact of short-term environmental exposure differences. End-tidal breath was collected as the primary analytical sample using disposable polyether ether ketone (PEEK) sampling bags, and HPPI-TOFMS analysis was completed within 1-3 hours after collection. Raw mass spectrometry data were processed by denoising, baseline correction, peak position calibration, peak detection, peak alignment, background subtraction, and interval normalization. A VOC feature matrix with mass-to-charge ratios (

resultsThe SCZ and HC groups had identical sex distributions (21 males and 13 females in each group). No significant differences were observed between the groups in age, BMI, or smoking status (all

conclusionsExhaled VOC analysis combined with machine learning can identify discriminative breath metabolomic features between patients with SCZ and HCs. Under the current sample size and internal validation framework, the RF model demonstrated promising discriminative performance and outperformed the linear PLS-DA model. These findings suggest that SCZ-related breath metabolomic information may be characterized more by multi-feature patterns than by isolated alterations in individual VOCs. The candidate VOC features and qualitative results in this study may provide valuable clues for future breath metabolomics research in schizophrenia.