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Metabolomic Profiling of #Plasma Reveals Differential #Disease Severity #Markers in avian #influenza A(#H7N9) infection Patients

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By G.M.

Highlights

• The characteristics of plasma metabolome in H7N9 patients were first revealed.

• It was discovered that lipid-like molecules were downregulated in death group.

• Metabolites of the tryptophan metabolic pathway were elevated in death group.

• The metabolite-based machine learning achieved an AUC of 0.929 on the test set.


Abstract

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.

Source: International Journal of Infectious Diseases, https://www.ijidonline.com/article/S1201-9712(25)00181-X/fulltext

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