Metabolomic analysis in severe childhood pneumonia in the Gambia, West Africa: findings from a pilot study.
<h4>Background</h4>Pneumonia remains the leading cause of death in young children globally and improved diagnostics are needed to better identify cases and reduce case fatality. Metabolomics, a rapidly evolving field aimed at characterizing metabolites in biofluids, has the potential to...
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Public Library of Science (PLoS)
2010-09-01
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| author | Evagelia C Laiakis Gerard A J Morris Albert J Fornace Stephen R C Howie |
| author_facet | Evagelia C Laiakis Gerard A J Morris Albert J Fornace Stephen R C Howie |
| author_sort | Evagelia C Laiakis |
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| description | <h4>Background</h4>Pneumonia remains the leading cause of death in young children globally and improved diagnostics are needed to better identify cases and reduce case fatality. Metabolomics, a rapidly evolving field aimed at characterizing metabolites in biofluids, has the potential to improve diagnostics in a range of diseases. The objective of this pilot study is to apply metabolomic analysis to childhood pneumonia to explore its potential to improve pneumonia diagnosis in a high-burden setting.<h4>Methodology/principal findings</h4>Eleven children with World Health Organization (WHO)-defined severe pneumonia of non-homogeneous aetiology were selected in The Gambia, West Africa, along with community controls. Metabolomic analysis of matched plasma and urine samples was undertaken using Ultra Performance Liquid Chromatography (UPLC) coupled to Time-of-Flight Mass Spectrometry (TOFMS). Biomarker extraction was done using SIMCA-P+ and Random Forests (RF). 'Unsupervised' (blinded) data were analyzed by Principal Component Analysis (PCA), while 'supervised' (unblinded) analysis was by Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structures (OPLS). Potential markers were extracted from S-plots constructed following analysis with OPLS, and markers were chosen based on their contribution to the variation and correlation within the data set. The dataset was additionally analyzed with the machine-learning algorithm RF in order to address issues of model overfitting and markers were selected based on their variable importance ranking. Unsupervised PCA analysis revealed good separation of pneumonia and control groups, with even clearer separation of the groups with PLS-DA and OPLS analysis. Statistically significant differences (p<0.05) between groups were seen with the following metabolites: uric acid, hypoxanthine and glutamic acid were higher in plasma from cases, while L-tryptophan and adenosine-5'-diphosphate (ADP) were lower; uric acid and L-histidine were lower in urine from cases. The key limitation of this study is its small size.<h4>Conclusions/significance</h4>Metabolomic analysis clearly distinguished severe pneumonia patients from community controls. The metabolites identified are important for the host response to infection through antioxidant, inflammatory and antimicrobial pathways, and energy metabolism. Larger studies are needed to determine whether these findings are pneumonia-specific and to distinguish organism-specific responses. Metabolomics has considerable potential to improve diagnostics for childhood pneumonia. |
| format | Article |
| id | doaj-art-9c6d5f9df2994be4bf4c423a94ddd60c |
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| language | English |
| publishDate | 2010-09-01 |
| publisher | Public Library of Science (PLoS) |
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| spelling | doaj-art-9c6d5f9df2994be4bf4c423a94ddd60c2025-08-20T02:31:51ZengPublic Library of Science (PLoS)PLoS ONE1932-62032010-09-0159e1265510.1371/journal.pone.0012655Metabolomic analysis in severe childhood pneumonia in the Gambia, West Africa: findings from a pilot study.Evagelia C LaiakisGerard A J MorrisAlbert J FornaceStephen R C Howie<h4>Background</h4>Pneumonia remains the leading cause of death in young children globally and improved diagnostics are needed to better identify cases and reduce case fatality. Metabolomics, a rapidly evolving field aimed at characterizing metabolites in biofluids, has the potential to improve diagnostics in a range of diseases. The objective of this pilot study is to apply metabolomic analysis to childhood pneumonia to explore its potential to improve pneumonia diagnosis in a high-burden setting.<h4>Methodology/principal findings</h4>Eleven children with World Health Organization (WHO)-defined severe pneumonia of non-homogeneous aetiology were selected in The Gambia, West Africa, along with community controls. Metabolomic analysis of matched plasma and urine samples was undertaken using Ultra Performance Liquid Chromatography (UPLC) coupled to Time-of-Flight Mass Spectrometry (TOFMS). Biomarker extraction was done using SIMCA-P+ and Random Forests (RF). 'Unsupervised' (blinded) data were analyzed by Principal Component Analysis (PCA), while 'supervised' (unblinded) analysis was by Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structures (OPLS). Potential markers were extracted from S-plots constructed following analysis with OPLS, and markers were chosen based on their contribution to the variation and correlation within the data set. The dataset was additionally analyzed with the machine-learning algorithm RF in order to address issues of model overfitting and markers were selected based on their variable importance ranking. Unsupervised PCA analysis revealed good separation of pneumonia and control groups, with even clearer separation of the groups with PLS-DA and OPLS analysis. Statistically significant differences (p<0.05) between groups were seen with the following metabolites: uric acid, hypoxanthine and glutamic acid were higher in plasma from cases, while L-tryptophan and adenosine-5'-diphosphate (ADP) were lower; uric acid and L-histidine were lower in urine from cases. The key limitation of this study is its small size.<h4>Conclusions/significance</h4>Metabolomic analysis clearly distinguished severe pneumonia patients from community controls. The metabolites identified are important for the host response to infection through antioxidant, inflammatory and antimicrobial pathways, and energy metabolism. Larger studies are needed to determine whether these findings are pneumonia-specific and to distinguish organism-specific responses. Metabolomics has considerable potential to improve diagnostics for childhood pneumonia.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0012655&type=printable |
| spellingShingle | Evagelia C Laiakis Gerard A J Morris Albert J Fornace Stephen R C Howie Metabolomic analysis in severe childhood pneumonia in the Gambia, West Africa: findings from a pilot study. PLoS ONE |
| title | Metabolomic analysis in severe childhood pneumonia in the Gambia, West Africa: findings from a pilot study. |
| title_full | Metabolomic analysis in severe childhood pneumonia in the Gambia, West Africa: findings from a pilot study. |
| title_fullStr | Metabolomic analysis in severe childhood pneumonia in the Gambia, West Africa: findings from a pilot study. |
| title_full_unstemmed | Metabolomic analysis in severe childhood pneumonia in the Gambia, West Africa: findings from a pilot study. |
| title_short | Metabolomic analysis in severe childhood pneumonia in the Gambia, West Africa: findings from a pilot study. |
| title_sort | metabolomic analysis in severe childhood pneumonia in the gambia west africa findings from a pilot study |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0012655&type=printable |
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