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<p>The discrepancy between serum triglyceride levels and the clinical severity of hyperlipidemic acute pancreatitis (HLAP) complicates risk stratification. Traditional lipidomics, which primarily rely on linear abundance, often fail to distinguish the HLAP-specific lipidome from the metabolic background of hypertriglyceridemia (HTG). To overcome this limitation, DeepLipiDecipher was developed as a knowledge-guided graph neural network framework that integrates lipid chemical structures with metabolic topology to identify latent lipotoxic features. In a retrospective cohort of 433 subjects, DeepLipiDecipher demonstrated robust classification performance (AUC = 0.810), effectively distinguishing the HLAP phenotype and outperforming conventional machine learning models. Interpretability analysis revealed that HLAP susceptibility correlated with a distinct structural lipid profile, marked by the synergistic enrichment of polyunsaturated and ether-linked phospholipids, rather than total lipid mass. Moreover, computational causal inference implicated a pathogenic mechanism wherein SMPD3-mediated ceramide accumulation induced basal cytotoxicity, and PTGS2 hyperactivation promoted the peroxidation of these vulnerable lipids, triggering systemic inflammation. These results highlight the value of incorporating network topology into lipidomic analysis and suggest the network-inferred SMPD3-Ceramide-PTGS2 immunometabolic axis as a potential therapeutic target for preventing the progression from metabolic dysfunction to acute organ injury.</p>

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Deep chemical structure graph learning deciphers the lipotoxicity code of hypertriglyceridemic pancreatitis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月27日

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  • Anliang Huang,
  • Qihang Yuan,
  • Junhong Chen,
  • Lei Wang,
  • Yanlong Yu,
  • Yunshu Zhang,
  • Shurong Ma,
  • Kai Liu,
  • Zeming Wu,
  • Shengji Cao,
  • Tianyi Liu,
  • Dong Shang,
  • Peiyuan Yin

摘要

The discrepancy between serum triglyceride levels and the clinical severity of hyperlipidemic acute pancreatitis (HLAP) complicates risk stratification. Traditional lipidomics, which primarily rely on linear abundance, often fail to distinguish the HLAP-specific lipidome from the metabolic background of hypertriglyceridemia (HTG). To overcome this limitation, DeepLipiDecipher was developed as a knowledge-guided graph neural network framework that integrates lipid chemical structures with metabolic topology to identify latent lipotoxic features. In a retrospective cohort of 433 subjects, DeepLipiDecipher demonstrated robust classification performance (AUC = 0.810), effectively distinguishing the HLAP phenotype and outperforming conventional machine learning models. Interpretability analysis revealed that HLAP susceptibility correlated with a distinct structural lipid profile, marked by the synergistic enrichment of polyunsaturated and ether-linked phospholipids, rather than total lipid mass. Moreover, computational causal inference implicated a pathogenic mechanism wherein SMPD3-mediated ceramide accumulation induced basal cytotoxicity, and PTGS2 hyperactivation promoted the peroxidation of these vulnerable lipids, triggering systemic inflammation. These results highlight the value of incorporating network topology into lipidomic analysis and suggest the network-inferred SMPD3-Ceramide-PTGS2 immunometabolic axis as a potential therapeutic target for preventing the progression from metabolic dysfunction to acute organ injury.