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Published on: November 21, 2013
Plasma Metabolic Characteristics and Potential Biomarker Combinations in Schizophrenia Patients With Tardive
Chenghao Lu1,2, Yeqing Dong1,2, Dan Qi1,2
1Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China.
Background And Hypothesis:
The pathogenesis of tardive dyskinesia (TD) remains unclear, involving multiple biological pathways. This study aimed to explore biomarkers of TD through untargeted metabolomics for the early identification of TD.
Study Design:
This study recruited 84 schizophrenia (SZ) patients with TD and 160 SZ patients without TD. TD diagnosis was based on the Schooler-Kane criteria, and the severity of TD and psychiatric symptoms were assessed using the Abnormal Involuntary Movement Scale and the Positive and Negative Syndrome Scale. Fasting blood samples were collected from all patients and subjected to untargeted metabolomics analysis using Ultra-high-performance liquid chromatography-high resolution mass spectrometry, allowing for the quantification and profiling of 699 metabolites. Data were analyzed with orthogonal partial least squares discriminant analysis, and receiver-operating characteristic curves.
Study Results:
In TD, 57 metabolites exhibited significant changes (variable importance of projection > 1, false discovery rate-adjusted P < .05), primarily involving amino acids and lipids. These changes predominantly affected the phenylalanine, tyrosine, and tryptophan pathway (impact = 0.5, P = .0252), as well as the phenylalanine metabolism pathway (impact = 0.36, P = .0498). N-Acetyl-l-phenylalanine (B = 2.249, t = 4.56, P < .001, 95% CI, 1.302-3.286) and Succinylcarnitine (AcCa(4:0-DC)) (B = 1.009, t = 3.07, P = .002, 95% CI, 0.362-1.656) are negatively related to the total abnormal involuntary movement scale score. Additionally, 5 differential metabolites had area under the curve (AUC) values greater than 0.7 for diagnosing TD, with the combined diagnostic capability exceeding 0.8 (AUC = 0.817, 95% CI, 0.759-0.875).
Conclusions:
In TD, disruptions in amino acid and lipid metabolism were predominantly observed. Amino acids and lipid metabolites may be involved in the development of TD. Additionally, a biomarker panel composed of amino acids and lipids can be used for the differential diagnosis of TD.
Insights
Untargeted metabolomics identified significant changes in amino acid and lipid metabolism in patients with tardive dyskinesia (TD). A panel of these metabolites shows potential as a biomarker for early TD diagnosis.
Area of Science:
- Neuroscience
- Biochemistry
- Metabolomics
Background:
- Tardive dyskinesia (TD) pathogenesis is complex and not fully understood.
- Multiple biological pathways are implicated in TD development.
- Early identification of TD is crucial for effective management.
Purpose of the Study:
- To explore potential biomarkers for tardive dyskinesia (TD) using untargeted metabolomics.
- To facilitate the early identification and diagnosis of TD.
- To investigate metabolic pathway disruptions in schizophrenia patients with TD.
Main Methods:
- Recruited 84 schizophrenia (SZ) patients with TD and 160 SZ patients without TD.
- Utilized untargeted metabolomics via ultra-high-performance liquid chromatography-high resolution mass spectrometry on blood samples.
- Analyzed 699 metabolites using multivariate statistical methods and receiver-operating characteristic curves.
Main Results:
- Identified 57 significantly altered metabolites in TD patients, primarily amino acids and lipids.
- Observed significant changes in phenylalanine, tyrosine, and tryptophan metabolic pathways.
- Developed a biomarker panel of 5 metabolites with a combined diagnostic AUC of 0.817 for TD.
Conclusions:
- Disruptions in amino acid and lipid metabolism are characteristic of tardive dyskinesia (TD).
- Specific amino acid and lipid metabolites are implicated in TD pathogenesis.
- A multi-metabolite biomarker panel can aid in the differential diagnosis of TD.
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