Identifying serum lipidomic signatures related to prognosis in first-episode schizophrenia

Antipsychotic medications are crucial for alleviating symptoms of schizophrenia (SCZ). However, treatment responses vary across individuals, and few reliable biomarkers currently exist to predict the clinical outcome. Therefore, we aim to identify potential lipid markers for treatment outcomes in patients with first-episode SCZ.

Pre-treatment serum samples were obtained from 95 participants who underwent an 8-week treatment regimen with antipsychotic drugs. Untargeted liquid chromatography-mass spectrometry (LC-MS) was used to acquire serum lipidomic profiles, correlating them with treatment responses at 8 weeks to identify potential lipid signatures. The antipsychotic treatment response was quantified using the percentage change on the Positive and Negative Syndrome Scale (PANSS) scale.

By combining LASSO regression and Random Forest regression, we identified 8 positively associated and 2 negatively associated baseline lipids related to the PANSS reduction rate. In the further analysis of logistic regression, we identified three candidate lipids, PC (18:2e_19:0), PE (53:7), and TG (16:2e_19:0_20:5), which could together distinguish poor and good responders, with an AUC of 0.805 (95% CI, 0.715-0.894).

Our findings suggest that this set of lipid biomarkers may have the potential to predict the outcome of antipsychotic drug treatment. Further validation and larger studies are needed to evaluate their potential for clinical applications.

Not applicable.