Progress in neuro-psychopharmacology & biological psychiatry · 2026
Baseline metabolic signature predicts cognitive improvement and longitudinal glycerophospholipid shifts track recovery in first-episode drug-naïve schizophrenia.
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Wang Shuo, Guo Yueru, Zhou Boxuan, Li Meijuan, Su Qiao, Qiu Yuying, Dong Yeqing, Li Jie
Аннотация
BACKGROUND: Cognitive impairment is a core yet treatment-resistant feature of schizophrenia (SZ), and reliable biomarkers predicting cognitive improvement remain scarce. This study identified a baseline plasma metabolic signature for predicting antipsychotic-induced cognitive improvement and elucidated the preliminary evidence for early cognitive improvement in first-episode drug-naïve SZ patients.
METHODS: In 105 first-episode drug-naïve SZ patients, targeted plasma metabolomics (Biocrates MxP Quant 500) was performed at baseline and week 8 of antipsychotic treatment. Patients were stratified by MATRICS Consensus Cognitive Battery (MCCB) T-score change. An ensemble machine learning framework combining LASSO, RF, and SVM-RFE identified consensus metabolites, with SHAP analysis for model interpretability. Longitudinal metabolic shifts were characterized via OPLS-DA and validated by propensity score matching (PSM).
RESULTS: Four baseline metabolites, TG (18:0_36:5), Serotonin, Isoleucine (Ile), and Tryptophan Betaine (TrpBetaine), constituted the predictive signature. The Random Forest classifier achieved an AUC of 0.734 (95% CI: 0.523-0.936); SHAP analysis indicated that higher TG (18:0_36:5) and Ile positively predicted improvement, while elevated Serotonin exerted inhibitory effects. Longitudinal analysis identified 20 differentially expressed metabolites predominantly enriched in glycerophospholipid metabolism. Increases in AABA correlated with working memory and neurocognitive gains, while reductions in PC aa C40:5 and PC aa C38:6 negatively associated with improved reasoning and problem-solving. PSM analyses suggested that measured baseline confounders did not fully explain these associations.
CONCLUSIONS: A four-metabolite plasma signature predicts antipsychotic-induced cognitive improvement in first-episode SZ, and glycerophospholipid dynamics track domain-specific cognitive recovery, offering novel short-term treatment-associated metabolic changes of cognitive impairment in SZ.
METHODS: In 105 first-episode drug-naïve SZ patients, targeted plasma metabolomics (Biocrates MxP Quant 500) was performed at baseline and week 8 of antipsychotic treatment. Patients were stratified by MATRICS Consensus Cognitive Battery (MCCB) T-score change. An ensemble machine learning framework combining LASSO, RF, and SVM-RFE identified consensus metabolites, with SHAP analysis for model interpretability. Longitudinal metabolic shifts were characterized via OPLS-DA and validated by propensity score matching (PSM).
RESULTS: Four baseline metabolites, TG (18:0_36:5), Serotonin, Isoleucine (Ile), and Tryptophan Betaine (TrpBetaine), constituted the predictive signature. The Random Forest classifier achieved an AUC of 0.734 (95% CI: 0.523-0.936); SHAP analysis indicated that higher TG (18:0_36:5) and Ile positively predicted improvement, while elevated Serotonin exerted inhibitory effects. Longitudinal analysis identified 20 differentially expressed metabolites predominantly enriched in glycerophospholipid metabolism. Increases in AABA correlated with working memory and neurocognitive gains, while reductions in PC aa C40:5 and PC aa C38:6 negatively associated with improved reasoning and problem-solving. PSM analyses suggested that measured baseline confounders did not fully explain these associations.
CONCLUSIONS: A four-metabolite plasma signature predicts antipsychotic-induced cognitive improvement in first-episode SZ, and glycerophospholipid dynamics track domain-specific cognitive recovery, offering novel short-term treatment-associated metabolic changes of cognitive impairment in SZ.