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. 2012 Aug 14;2(8):e149.
doi: 10.1038/tp.2012.76.

Schizophrenia shows a unique metabolomics signature in plasma

Y He  1 , Z Yu, I Giegling, L Xie, A M Hartmann, C Prehn, J Adamski, R Kahn, Y Li, T Illig, R Wang-Sattler, D Rujescu
Affiliations

Schizophrenia shows a unique metabolomics signature in plasma

Y He et al. Transl Psychiatry. .

Abstract

Schizophrenia is a severe complex mental disorder affecting 0.5-1% of the world population. To date, diagnosis of the disease is mainly based on personal and thus subjective interviews. The underlying molecular mechanism of schizophrenia is poorly understood. Using targeted metabolomics we quantified and compared 103 metabolites in plasma samples from 216 healthy controls and 265 schizophrenic patients, including 52 cases that do not take antipsychotic medication. Compared with healthy controls, levels of five metabolites were found significantly altered in schizophrenic patients (P-values ranged from 2.9 × 10(-8) to 2.5 × 10(-4)) and in neuroleptics-free probands (P-values ranging between 0.006 and 0.03), respectively. These metabolites include four amino acids (arginine, glutamine, histidine and ornithine) and one lipid (PC ae C38:6) and are suggested as candidate biomarkers for schizophrenia. To explore the genetic susceptibility on the associated metabolic pathways, we constructed a molecular network connecting these five aberrant metabolites with 13 schizophrenia risk genes. Our result implicated aberrations in biosynthetic pathways linked to glutamine and arginine metabolism and associated signaling pathways as genetic risk factors, which may contribute to patho-mechanisms and memory deficits associated with schizophrenia. This study illustrated that the metabolic deviations detected in plasma may serve as potential biomarkers to aid diagnosis of schizophrenia.

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Figures

Figure 1
Figure 1
Metabolomics discriminate schizophrenic patients from healthy individuals. (a) Partial least square (PLS) analysis demonstrate a clear separation of metabolite concentration between patients with schizophrenia (red) and healthy individuals (black). (b) The concentrations of five metabolite signatures were shown in beanplots, which provide information on the mean level (solid black line), individual data points (short gray lines) and the density of the distribution between controls and cases. (c) The receiver-operating characteristic (ROC) curves indicated the effect of discrimination based on the combination of five metabolite signatures in the model (red) and the combination of age, sex and BMI (green) with 95% confidence intervals s.e. of the true-positive rate.
Figure 2
Figure 2
Concept of molecular network in schizophrenia. The network showed associations between the five candidate metabolites and the schizophrenia risk genes. For details, see Supplementary Table 2. Node shaped as circle, square, octagon and diamond represent metabolite, enzyme, intermediate protein and schizophrenia risk gene, respectively. Nodes overlaid with a color key representing their major functions: arginine, glutamine metabolism process and nitrogen compound biosynthetic process (yellow); signaling pathway (blue); memory or learning (purple); proliferation, apoptosis and focal adhesion (gray).
Figure 3
Figure 3
Influence of genetic risks on the metabolic pathways. The schizophrenia risk genes (marked by red stars) are supposed to be involved in the regulation of these metabolic pathways (black). Types of regulations are visualized as following: activation (red), inhibition (blue), neurotrophin signaling (purple) and physical interaction (gray). The observed metabolite concentration changes between schizophrenics and healthy individuals are denoted by the red and green arrows next to them. Blue arrows suggest the possible alteration reported in previous publications. Bigger/smaller arrows indicate reported/observed changes in metabolite concentrations or gene/protein level.
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