Shuo Wang , Yeqing Dong , Yuying Qiu , Xiaoxiao Sun, Changyong Jiang, Qiao Su, Meijuan Li, Jie Li
{"title":"Prediction of treatment response in drug-naïve schizophrenia patients from the perspective of targeted metabolomics","authors":"Shuo Wang , Yeqing Dong , Yuying Qiu , Xiaoxiao Sun, Changyong Jiang, Qiao Su, Meijuan Li, Jie Li","doi":"10.1016/j.schres.2025.03.016","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":21417,"journal":{"name":"Schizophrenia Research","volume":"278 ","pages":"Pages 9-16"},"PeriodicalIF":3.6000,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Schizophrenia Research","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0920996425000799","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
As official journal of the Schizophrenia International Research Society (SIRS) Schizophrenia Research is THE journal of choice for international researchers and clinicians to share their work with the global schizophrenia research community. More than 6000 institutes have online or print (or both) access to this journal - the largest specialist journal in the field, with the largest readership!
Schizophrenia Research''s time to first decision is as fast as 6 weeks and its publishing speed is as fast as 4 weeks until online publication (corrected proof/Article in Press) after acceptance and 14 weeks from acceptance until publication in a printed issue.
The journal publishes novel papers that really contribute to understanding the biology and treatment of schizophrenic disorders; Schizophrenia Research brings together biological, clinical and psychological research in order to stimulate the synthesis of findings from all disciplines involved in improving patient outcomes in schizophrenia.