Frontiers in pharmacology · 2025
Serum metabolomics identifies novel prognostic biomarkers in amanita poisoning.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhu Dan, Zhong Jie, Liu Yarong, Zhang Sicheng, Zou Lianhong
Аннотация
BACKGROUND: Amanita poisoning causes 90%-95% of global mushroom-related deaths, yet early prognostic biomarkers for Amanita poisoning are lacking.
METHODS: 33 patients with Amanita poisoning were recruited and categorized into survival and death group. Multivariate logistic regression analysis was used to investigate the independent mortality risk factors for Amanita poisoning patients. Untargeted serum metabolomics was performed to screen the differentially expressed metabolites. The quality control samples were used to evaluate the stability and reproducibility of ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS) analytical system. The prognosis predictive metabolic biomarkers were identified by ROC curve analysis. Correlations between metabolic biomarkers and biochemical indicators were analyzed by Spearman's correlation analysis.
RESULTS: Significant differences were observed between the survival and death groups in clinical manifestations-such as gastrointestinal bleeding, dizziness, headache, delirious coma, infection, and shortness of breath-and in biochemical indicators, including alanine transaminase (ALT), aspartate transaminase (AST), prothrombin time and activated partial thromboplastin time (APTT). Metabolomic analysis identified 80 differentially expressed metabolites involved primarily in amino acid and unsaturated fatty acid metabolism. ROC analysis (AUC >0.9) screened nine potential metabolic biomarkers for predicting clinical outcomes: 9,10-Epoxyoctadecenoic acid, Phosphatidylinositol(16:0/18:2 (9Z,12Z)), N-Acetyl-L-aspartic acid, PI(20:3 (5Z,8Z,11Z)/18:0), Propionylcarnitine, Proline betaine, 4'-Methyl-(-)-epigallocatechin 3-(4-methyl-gallate), PG (18:1 (11Z)/22:6 (4Z,7Z,10Z,13Z,16Z,19Z)), and L-Proline. Notably, correlation analysis revealed that 9,10-Epoxyoctadecenoic acid was positively correlated with AST and activated partial thromboplastin time, whereas 4'-Methyl-(-)-epigallocatechin 3-(4-methyl-gallate), N-acetyl-L-aspartic acid, PI(16:0/18:2 (9Z,12Z)), PI(20:3 (5Z,8Z,11Z)/18:0), and Propionylcarnitine showed negative correlations with various liver and coagulation parameters.
CONCLUSION: Serum metabolomics has identified metabolic biomarkers capable of predicting mortality in Amanita poisoning, with significant correlation to liver and coagulation injury. These biomarkers may facilitate early risk stratification and guide targeted therapeutic interventions. Limitations include small sample size and single-center retrospective design, which may restrict result generalizability.
METHODS: 33 patients with Amanita poisoning were recruited and categorized into survival and death group. Multivariate logistic regression analysis was used to investigate the independent mortality risk factors for Amanita poisoning patients. Untargeted serum metabolomics was performed to screen the differentially expressed metabolites. The quality control samples were used to evaluate the stability and reproducibility of ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS) analytical system. The prognosis predictive metabolic biomarkers were identified by ROC curve analysis. Correlations between metabolic biomarkers and biochemical indicators were analyzed by Spearman's correlation analysis.
RESULTS: Significant differences were observed between the survival and death groups in clinical manifestations-such as gastrointestinal bleeding, dizziness, headache, delirious coma, infection, and shortness of breath-and in biochemical indicators, including alanine transaminase (ALT), aspartate transaminase (AST), prothrombin time and activated partial thromboplastin time (APTT). Metabolomic analysis identified 80 differentially expressed metabolites involved primarily in amino acid and unsaturated fatty acid metabolism. ROC analysis (AUC >0.9) screened nine potential metabolic biomarkers for predicting clinical outcomes: 9,10-Epoxyoctadecenoic acid, Phosphatidylinositol(16:0/18:2 (9Z,12Z)), N-Acetyl-L-aspartic acid, PI(20:3 (5Z,8Z,11Z)/18:0), Propionylcarnitine, Proline betaine, 4'-Methyl-(-)-epigallocatechin 3-(4-methyl-gallate), PG (18:1 (11Z)/22:6 (4Z,7Z,10Z,13Z,16Z,19Z)), and L-Proline. Notably, correlation analysis revealed that 9,10-Epoxyoctadecenoic acid was positively correlated with AST and activated partial thromboplastin time, whereas 4'-Methyl-(-)-epigallocatechin 3-(4-methyl-gallate), N-acetyl-L-aspartic acid, PI(16:0/18:2 (9Z,12Z)), PI(20:3 (5Z,8Z,11Z)/18:0), and Propionylcarnitine showed negative correlations with various liver and coagulation parameters.
CONCLUSION: Serum metabolomics has identified metabolic biomarkers capable of predicting mortality in Amanita poisoning, with significant correlation to liver and coagulation injury. These biomarkers may facilitate early risk stratification and guide targeted therapeutic interventions. Limitations include small sample size and single-center retrospective design, which may restrict result generalizability.