tetano
Editor, Senior Moderator
Int J Infect Dis
. 2025 Jun 17:107957.
doi: 10.1016/j.ijid.2025.107957. Online ahead of print. Metabolomic Profiling of Plasma Reveals Differential Disease Severity Markers in avian influenza A(H7N9) infection Patients
Yuefeng Wang[SUP] 1 [/SUP], Jili Ni[SUP] 2 [/SUP], Mingzhu Huang[SUP] 2 [/SUP], Wenxin Qu[SUP] 2 [/SUP], Chang Liu[SUP] 2 [/SUP], Zheying Mao[SUP] 2 [/SUP], Jiaqi Bao[SUP] 3 [/SUP], Weizhen Chen[SUP] 3 [/SUP], Dongsheng Han[SUP] 3 [/SUP], Fei Yu[SUP] 3 [/SUP], Yifei Shen[SUP] 3 [/SUP], Zhenzhen Deng[SUP] 4 [/SUP], Shufa Zheng[SUP] 5 [/SUP]
Affiliations
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.
Conclusions: 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.
Keywords: H7N9; Metabolomic; Tryptophan metabolism; death prediction model; machine learning.
. 2025 Jun 17:107957.
doi: 10.1016/j.ijid.2025.107957. Online ahead of print. Metabolomic Profiling of Plasma Reveals Differential Disease Severity Markers in avian influenza A(H7N9) infection Patients
Yuefeng Wang[SUP] 1 [/SUP], Jili Ni[SUP] 2 [/SUP], Mingzhu Huang[SUP] 2 [/SUP], Wenxin Qu[SUP] 2 [/SUP], Chang Liu[SUP] 2 [/SUP], Zheying Mao[SUP] 2 [/SUP], Jiaqi Bao[SUP] 3 [/SUP], Weizhen Chen[SUP] 3 [/SUP], Dongsheng Han[SUP] 3 [/SUP], Fei Yu[SUP] 3 [/SUP], Yifei Shen[SUP] 3 [/SUP], Zhenzhen Deng[SUP] 4 [/SUP], Shufa Zheng[SUP] 5 [/SUP]
Affiliations
- PMID: 40553895
- DOI: 10.1016/j.ijid.2025.107957
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.
Conclusions: 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.
Keywords: H7N9; Metabolomic; Tryptophan metabolism; death prediction model; machine learning.