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Войти в исследования International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2025
Metabolomic profiling of plasma reveals differential disease severity markers in avian influenza A(H7N9) infection patients.
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Wang Yuefeng, Ni Jili, Huang Mingzhu, Qu Wenxin, Liu Chang, Mao Zheying, Bao Jiaqi, Chen Weizhen, Han Dongsheng, Yu Fei, Shen Yifei, Deng Zhenzhen, Zheng Shufa
Аннотация
OBJECTIVES: Avian influenza such as H7N9 is currently a major global public health risk, and at present, there is a lack of relevant diagnostic and treatment markers.
METHODS: We collected plasma samples from 104 confirmed H7N9 patients, 31 of whom died. Plasma metabolites were detected by UHPLC-HRMS, and a survival prediction model based on metabolites was constructed by machine-learning models.
RESULTS: A total of 1536 metabolites were identified in the plasma samples of H7N9 patients, of which 64 metabolites were up-regulated and 35 metabolites were down-regulated in the death group. The enrichment analysis of tryptophan metabolism, porphyrin metabolism, and riboflavin metabolism were significantly up-regulated in the death group. We found that most lipids and lipid-like molecules were down-regulated in the death group, and organoheterocyclic compounds were significantly up-regulated in the death group. A machine-learning model was constructed for predicting mortality based on porphobilinogen, 5-hydroxyindole-3-acetic acid, L-kynurenine, Biliverdin, and D-dimer. The AUC on the test set was 0.929.
CONCLUSION: We first revealed the plasma metabolomic characteristics of H7N9 patients and found that a machine-learning model based on plasma metabolites could predict the risk of death for H7N9 in the early stage of admission.
METHODS: We collected plasma samples from 104 confirmed H7N9 patients, 31 of whom died. Plasma metabolites were detected by UHPLC-HRMS, and a survival prediction model based on metabolites was constructed by machine-learning models.
RESULTS: A total of 1536 metabolites were identified in the plasma samples of H7N9 patients, of which 64 metabolites were up-regulated and 35 metabolites were down-regulated in the death group. The enrichment analysis of tryptophan metabolism, porphyrin metabolism, and riboflavin metabolism were significantly up-regulated in the death group. We found that most lipids and lipid-like molecules were down-regulated in the death group, and organoheterocyclic compounds were significantly up-regulated in the death group. A machine-learning model was constructed for predicting mortality based on porphobilinogen, 5-hydroxyindole-3-acetic acid, L-kynurenine, Biliverdin, and D-dimer. The AUC on the test set was 0.929.
CONCLUSION: We first revealed the plasma metabolomic characteristics of H7N9 patients and found that a machine-learning model based on plasma metabolites could predict the risk of death for H7N9 in the early stage of admission.