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<p>Nearly 30% of patients with schizophrenia respond inadequately to current antipsychotics, with unclear markers and mechanisms of antipsychotic efficacy. A total of 208 patients with schizophrenia treated for 6 weeks with oral paliperidone were analyzed through genotyping, mass spectrometry proteomic, and metabolomic profiling to explore underlying markers and mechanisms of antipsychotic efficacy. Machine learning analysis identified 20 proteins and 20 metabolites at baseline predictive of treatment response. Proteomic and metabolomic models achieved a cross-site mean AUC of 0.923 and 0.816, respectively. A multi-omics ensemble model achieved 0.941. GWAS and differential analyses identified 32 loci (<i>P</i> &lt; 5 × 10<sup>−5</sup>), 83 proteins, and 31 metabolites associated with efficacy (<i>P</i> &lt; 0.05). Trans-omics analysis of these efficacy-related molecules across three omic layers highlighted glycerophospholipid metabolism (<i>P</i> = 3.25 × 10<sup>−5</sup>) and sphingolipid metabolism (<i>P</i> = 0.039). Key molecules within these pathways exhibited a consistent direction of effect in regulating phosphatidylcholine (PC) and sphingomyelin (SM) metabolism, and higher PC and SM levels were found to correlate with better efficacy. These associations were further genetically validated using polygenic risk scores in two independent cohorts (2281 and 449 patients, respectively). In conclusion, multi-omics modeling is able to accurately identify antipsychotic efficacy, and higher PC and SM levels correlate with better antipsychotic efficacy, suggesting that variations in phospholipid metabolism may underlie the response to antipsychotics.</p>

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Multi-omics reveal critical roles of phosphatidylcholine and sphingomyelin in antipsychotic efficacy for schizophrenia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月13日

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  • Junyuan Sun,
  • Zhe Lu,
  • Zhewei Kang,
  • Yuyanan Zhang,
  • Yaoyao Sun,
  • Guorui Zhao,
  • Qijing Bo,
  • Wenqiang Li,
  • Zhenghui Yi,
  • Binbin Chen,
  • Yuandong Gong,
  • Zhenhe Zhou,
  • Huiling Wang,
  • Lin Lu,
  • Weihua Yue

摘要

Nearly 30% of patients with schizophrenia respond inadequately to current antipsychotics, with unclear markers and mechanisms of antipsychotic efficacy. A total of 208 patients with schizophrenia treated for 6 weeks with oral paliperidone were analyzed through genotyping, mass spectrometry proteomic, and metabolomic profiling to explore underlying markers and mechanisms of antipsychotic efficacy. Machine learning analysis identified 20 proteins and 20 metabolites at baseline predictive of treatment response. Proteomic and metabolomic models achieved a cross-site mean AUC of 0.923 and 0.816, respectively. A multi-omics ensemble model achieved 0.941. GWAS and differential analyses identified 32 loci (P < 5 × 10−5), 83 proteins, and 31 metabolites associated with efficacy (P < 0.05). Trans-omics analysis of these efficacy-related molecules across three omic layers highlighted glycerophospholipid metabolism (P = 3.25 × 10−5) and sphingolipid metabolism (P = 0.039). Key molecules within these pathways exhibited a consistent direction of effect in regulating phosphatidylcholine (PC) and sphingomyelin (SM) metabolism, and higher PC and SM levels were found to correlate with better efficacy. These associations were further genetically validated using polygenic risk scores in two independent cohorts (2281 and 449 patients, respectively). In conclusion, multi-omics modeling is able to accurately identify antipsychotic efficacy, and higher PC and SM levels correlate with better antipsychotic efficacy, suggesting that variations in phospholipid metabolism may underlie the response to antipsychotics.