Metabolomic profiling of plasma reveals differential disease severity markers in avian influenza A(H7N9) infection patients
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-HRM...
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Elsevier
2025-09-01
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| Series: | International Journal of Infectious Diseases |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S120197122500181X |
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| author | Yuefeng Wang Jili Ni Mingzhu Huang Wenxin Qu Chang Liu Zheying Mao Jiaqi Bao Weizhen Chen Dongsheng Han Fei Yu Yifei Shen Zhenzhen Deng Shufa Zheng |
| author_facet | Yuefeng Wang Jili Ni Mingzhu Huang Wenxin Qu Chang Liu Zheying Mao Jiaqi Bao Weizhen Chen Dongsheng Han Fei Yu Yifei Shen Zhenzhen Deng Shufa Zheng |
| author_sort | Yuefeng Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-32c0babf52ba47a3b62194ddb094e295 |
| institution | DOAJ |
| issn | 1201-9712 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Infectious Diseases |
| spelling | doaj-art-32c0babf52ba47a3b62194ddb094e2952025-08-20T03:18:38ZengElsevierInternational Journal of Infectious Diseases1201-97122025-09-0115810795710.1016/j.ijid.2025.107957Metabolomic profiling of plasma reveals differential disease severity markers in avian influenza A(H7N9) infection patientsYuefeng Wang0Jili Ni1Mingzhu Huang2Wenxin Qu3Chang Liu4Zheying Mao5Jiaqi Bao6Weizhen Chen7Dongsheng Han8Fei Yu9Yifei Shen10Zhenzhen Deng11Shufa Zheng12Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Blood Transfusion, Shaoxing Maternity and Child Health Care Hospital, Shaoxing, Zhejiang, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, Zhejiang, China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou, Zhejiang, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, Zhejiang, China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou, Zhejiang, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, Zhejiang, China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou, Zhejiang, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, Zhejiang, China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou, Zhejiang, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, Zhejiang, China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou, Zhejiang, ChinaHangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, Zhejiang, China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou, Zhejiang, China; State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Corresponding author: Shufa Zheng, Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou 310003, China.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.http://www.sciencedirect.com/science/article/pii/S120197122500181XH7N9MetabolomicMachine-learningDeath prediction modelTryptophan metabolism |
| spellingShingle | Yuefeng Wang Jili Ni Mingzhu Huang Wenxin Qu Chang Liu Zheying Mao Jiaqi Bao Weizhen Chen Dongsheng Han Fei Yu Yifei Shen Zhenzhen Deng Shufa Zheng Metabolomic profiling of plasma reveals differential disease severity markers in avian influenza A(H7N9) infection patients International Journal of Infectious Diseases H7N9 Metabolomic Machine-learning Death prediction model Tryptophan metabolism |
| title | Metabolomic profiling of plasma reveals differential disease severity markers in avian influenza A(H7N9) infection patients |
| title_full | Metabolomic profiling of plasma reveals differential disease severity markers in avian influenza A(H7N9) infection patients |
| title_fullStr | Metabolomic profiling of plasma reveals differential disease severity markers in avian influenza A(H7N9) infection patients |
| title_full_unstemmed | Metabolomic profiling of plasma reveals differential disease severity markers in avian influenza A(H7N9) infection patients |
| title_short | Metabolomic profiling of plasma reveals differential disease severity markers in avian influenza A(H7N9) infection patients |
| title_sort | metabolomic profiling of plasma reveals differential disease severity markers in avian influenza a h7n9 infection patients |
| topic | H7N9 Metabolomic Machine-learning Death prediction model Tryptophan metabolism |
| url | http://www.sciencedirect.com/science/article/pii/S120197122500181X |
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