敗血症関連脳症における主要代謝物としてのスクシネートを特定する広範標的メタボロミクスと機械学習

Sepsis-associated encephalopathy (SAE) is a common and serious complication of sepsis that leads to acute brain dysfunction and long-term cognitive impairment. We used widely targeted LC-MS/MS plasma metabolomics in 29 healthy controls, 32 sepsis patients, and 27 SAE patients, combined with machine learning, to define metabolic patterns across these groups. This approach identified 12 discriminatory metabolites, with succinate showing a stepwise increase from health to sepsis to SAE and associations with clinical severity scores. To test its functional relevance, we used a cecal ligation and puncture (CLP) mouse model and found that exogenous succinate supplementation aggravated cognitive deficits, neuronal injury, and microglial activation. Together, these findings link systemic metabolic remodeling to brain inflammation and dysfunction in sepsis and suggest that succinate and related pathways may help stratify SAE risk and provide mechanistic entry points for future therapeutic exploration.