Schizophrenia is a severe mental disorder that affects 0.5–1% of the population worldwide. Current diagnostic methods are based on psychiatric interviews, which are subjective in nature. The lack of disease biomarkers to support objective laboratory tests has been a long-standing bottleneck in the clinical diagnosis and evaluation of schizophrenia. Here we report a global metabolic profiling study involving 112 schizophrenic patients and 110 healthy subjects, who were divided into a training set and a test set, designed to identify metabolite markers. A panel of serum markers consisting of glycerate, eicosenoic acid, β-hydroxybutyrate, pyruvate and cystine was identified as an effective diagnostic tool, achieving an area under the receiver operating characteristic curve (AUC) of 0.945 in the training samples (62 patients and 62 controls) and 0.895 in the test samples (50 patients and 48 controls). Furthermore, a composite panel by the addition of urine β-hydroxybutyrate to the serum panel achieved a more satisfactory accuracy, which reached an AUC of 1 in both the training set and the test set. Multiple fatty acids and ketone bodies were found significantly (P<0.01) elevated in both the serum and urine of patients, suggesting an upregulated fatty acid catabolism, presumably resulting from an insufficiency of glucose supply in the brains of schizophrenia patients.

Schizophrenia is a complex mental disease caused by a combination of serial alterations in genetic and environmental factors. Although the brain is usually considered as the most relevant organ in schizophrenia, accumulated evidence suggests that peripheral tissues also contribute to this disease. In particular, abnormalities of the immune system have been identified in the peripheral blood of schizophrenia patients. To screen the serum proteomic signature of schizophrenia patients, we conducted shotgun proteomic analysis on serum samples of schizophrenia patients and healthy controls. High-abundance proteins were eliminated by immunoaffinity before LC-MS/MS analysis. The multivariate statistical test partial least squares-discriminant analysis (PLS-DA) was applied to build models for screening out variable importance in the projection (VIP) and 27 proteins were identified as being responsible for discriminating between the proteomic profiles of schizophrenia patients and healthy controls. Pathway analysis based on these 27 proteins revealed that complement and coagulation cascades was the most significant pathway. ELISA-based activity analyses indicated that the alternative complement pathway was suppressed in schizophrenia patients. Ingenuity pathways analysis was used to conduct the interaction network of 27 proteins. The network exhibited common features such as, nervous system development and function, humoral immune response and inflammatory response, and highlighted some proteins with important roles in the immune system, such as hub nodes. Our findings indicate dysregulation of the alternative complement pathway in schizophrenia patients. The protein interaction network enhances the interpretation of proteomic data and provides evidence that the immune system may contribute to schizophrenia.