ArticleFrontiers in molecular biosciences2026
Predictive role of tear metabolomics in delirium during anesthesia emergence and postoperative period in elderly patients after abdominal surgery.
Article in Frontiers in molecular biosciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To investigate characteristic tear metabolite changes in patients with postoperative delirium using tear metabolomic analysis and evaluate their potential as biomarkers for diagnosing delirium. Methods: In this study, 141 elderly patients undergoing abdominal surgery were enrolled. Tear samples were collected after anesthesia induction and before completion of surgical suturing. Delirium was assessed as emergence delirium (ED) in the PACU and ward delirium (WD) during the postoperative ward stay. Tear metabolites were analyzed by ultra-high-performance liquid chromatography-Orbitrap exploris-mass spectrometry. Results: Based on the occurrence of ED and WD, patients were stratified into two matched pairs of subgroups: the ED group (n = 32) and its matched non-ED control group (n = 32), while the WD group (n = 34) and its matched non-WD control group (n = 34), respectively. Metabolomic analysis showed that five and three important metabolic pathways were respectively associated with ED and WD. A decrease in arachidonic acid levels before surgery and an increase in dopamine levels after surgery may predict ED occurrence, whereas a decrease in glyoxylic acid levels after surgery may predict WD. The receiver operating characteristic curve analysis yielded an area under the curve values of 0.88 (95%CI: 0.78-0.97) and 0.88 (95%CI: 0.80-0.97) for preoperative and postoperative differential metabolite ED models, respectively. The WD model predictive efficiency reached 0.75 (95%CI: 0.63-0.87) and 0.78 (95%CI: 0.67-0.89), respectively. Conclusion: In this study, changes in metabolic pathways and metabolite levels in tears associated with postoperative delirium were identified. The delirium prediction models established based on tear metabolomics show good predictive efficacy.
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