Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza
| Autor: | ChunHong Huang, Andrew Y. Ng, Natasha Garamani, Manhong Wu, Tina M. Cowan, Mona Wood, Benjamin A. Pinsky, Hari Sowrirajan, Malaya K. Sahoo, Catherine A. Hogan, Nick A. Phillips, Justin Mak, Anthony Le, Pranav Rajpurkar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Adult
Male Medicine (General) Adolescent Metabolite Machine learning computer.software_genre General Biochemistry Genetics and Molecular Biology Gas Chromatography-Mass Spectrometry Machine Learning chemistry.chemical_compound R5-920 Diagnosis Influenza Human Host response Medicine Metabolomics Humans Biomarker discovery Respiratory system Prospective cohort study Child Receiver operating characteristic Mass spectrometry business.industry General Medicine Orthomyxoviridae Confidence interval Influenza Pyrrolidonecarboxylic Acid Nasal Mucosa chemistry Molecular Diagnostic Techniques Child Preschool Metabolome Respiratory virus Biomarker (medicine) Female Artificial intelligence business computer Research Paper |
| Zdroj: | EBioMedicine EBioMedicine, Vol 71, Iss, Pp 103546-(2021) |
| ISSN: | 2352-3964 |
| Popis: | Background Respiratory virus infections are significant causes of morbidity and mortality, and may induce host metabolite alterations by infecting respiratory epithelial cells. We investigated the use of liquid chromatography quadrupole time-of-flight mass spectrometry (LC/Q-TOF) combined with machine learning for the diagnosis of influenza infection. Methods We analyzed nasopharyngeal swab samples by LC/Q-TOF to identify distinct metabolic signatures for diagnosis of acute illness. Machine learning models were performed for classification, followed by Shapley additive explanation (SHAP) analysis to analyze feature importance and for biomarker discovery. Findings A total of 236 samples were tested in the discovery phase by LC/Q-TOF, including 118 positive samples (40 influenza A 2009 H1N1, 39 influenza H3 and 39 influenza B) as well as 118 age and sex-matched negative controls with acute respiratory illness. Analysis showed an area under the receiver operating characteristic curve (AUC) of 1.00 (95% confidence interval [95% CI] 0.99, 1.00), sensitivity of 1.00 (95% CI 0.86, 1.00) and specificity of 0.96 (95% CI 0.81, 0.99). The metabolite most strongly associated with differential classification was pyroglutamic acid. Independent validation of a biomarker signature based on the top 20 differentiating ion features was performed in a prospective cohort of 96 symptomatic individuals including 48 positive samples (24 influenza A 2009 H1N1, 5 influenza H3 and 19 influenza B) and 48 negative samples. Testing performed using a clinically-applicable targeted approach, liquid chromatography triple quadrupole mass spectrometry, showed an AUC of 1.00 (95% CI 0.998, 1.00), sensitivity of 0.94 (95% CI 0.83, 0.98), and specificity of 1.00 (95% CI 0.93, 1.00). Limitations include lack of sample suitability assessment, and need to validate these findings in additional patient populations. Interpretation This metabolomic approach has potential for diagnostic applications in infectious diseases testing, including other respiratory viruses, and may eventually be adapted for point-of-care testing. Funding None. |
| Databáze: | OpenAIRE |
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