Schizophrenia research · 2025
Prediction of treatment response in drug-naïve schizophrenia patients from the perspective of targeted metabolomics.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Wang Shuo, Dong Yeqing, Qiu Yuying, Sun Xiaoxiao, Jiang Changyong, Su Qiao, Li Meijuan, Li Jie
Аннотация
BACKGROUND: Schizophrenia (SZ) is a severe and chronic mental illness affecting approximately 1 % of the global population. Although antipsychotic medications can alleviate some symptoms, 20--30 % of patients exhibit resistance to available treatments. Therefore, identifying objective biomarkers related to treatment efficacy is crucial.
METHODS: A total of 56 drug-naïve SZ patients were recruited, and after 8 weeks of antipsychotic medication, they were classified as treatment responders (30) and non-responders (26) based on the improvement of their symptoms. Baseline plasma metabolites were measured by targeted metabolomics Biocrates MxP® Quant 500 Kit.
RESULTS: A total of 271 metabolites were identified, among which 31 exhibited significant differences between responders and non-responders, including phosphatidylcholine (PC) (14), sphingomyelin (8), ceramide (6), cholesteryl ester (2), and sarcosine (1), which were mainly concentrated in the sphingolipid metabolic pathway. Notably, key differential metabolites included phosphatidylcholine, sphingomyelin, and ceramide, which were predominantly enriched in the sphingolipid metabolism pathway. Through logistic regression analysis, sarcosine, PC aa C28:1, PC ae C34:2, and PC ae C36:3 emerged as significant predictors, yielding a combined area under the curve (AUC) of 0.877 for effectively distinguishing treatment responders from non-responders.
CONCLUSION: Our findings suggest that the combination of sarcosine, PC aa C28:1, PC ae C34:2, and PC ae C36:3 could serve as biomarkers for prediction of treatment response in patients with drug-naïve SZ.
METHODS: A total of 56 drug-naïve SZ patients were recruited, and after 8 weeks of antipsychotic medication, they were classified as treatment responders (30) and non-responders (26) based on the improvement of their symptoms. Baseline plasma metabolites were measured by targeted metabolomics Biocrates MxP® Quant 500 Kit.
RESULTS: A total of 271 metabolites were identified, among which 31 exhibited significant differences between responders and non-responders, including phosphatidylcholine (PC) (14), sphingomyelin (8), ceramide (6), cholesteryl ester (2), and sarcosine (1), which were mainly concentrated in the sphingolipid metabolic pathway. Notably, key differential metabolites included phosphatidylcholine, sphingomyelin, and ceramide, which were predominantly enriched in the sphingolipid metabolism pathway. Through logistic regression analysis, sarcosine, PC aa C28:1, PC ae C34:2, and PC ae C36:3 emerged as significant predictors, yielding a combined area under the curve (AUC) of 0.877 for effectively distinguishing treatment responders from non-responders.
CONCLUSION: Our findings suggest that the combination of sarcosine, PC aa C28:1, PC ae C34:2, and PC ae C36:3 could serve as biomarkers for prediction of treatment response in patients with drug-naïve SZ.