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Updated: Jan 10, 2026

Lipid Droplet Isolation for Quantitative Mass Spectrometry Analysis
Published on: April 17, 2017
Dengue virus infection in children: Serum lipidomics profiling for biomarker discovery
Ricardo E Correa Fierro1, Noroska Gabriela Mogollón Salazar2, Washington B Cárdenas1
1Laboratorio para Ensayos Biológicos, Biológicos y Diagnósticos-ENBIDIA, Facultad de Ciencias de la Vida, ESPOL, Guayaquil, Ecuador.
Insights
Pediatric dengue fever causes significant lipid metabolism changes. Lipidomics identified potential biomarkers, including specific metabolites, for improved dengue diagnosis in children and adolescents.
Area of Science:
- Biochemistry
- Immunology
- Metabolomics
Background:
- Dengue fever is a global health issue, especially in tropical regions, impacting children and adolescents.
- Dengue virus (DENV) infection causes immune responses leading to metabolic changes, particularly in lipid metabolism.
- Novel biomarkers are needed for faster dengue diagnosis and patient screening.
Purpose of the Study:
- To investigate lipid metabolism alterations in pediatric dengue fever using a lipidomics approach.
- To identify potential lipid biomarkers for dengue diagnosis in children and adolescents.
- To evaluate the diagnostic performance of identified metabolites.
Main Methods:
- Serum lipid metabolome analysis using liquid chromatography-mass spectrometry (LC-MS).
- Comparison of lipid profiles between DENV-infected children/adolescents (n=25) and healthy controls (n=15).
- Statistical analysis including partial least squares discriminant analysis (PLS-DA) and receiver operating characteristic (ROC) curve analysis.
Main Results:
- PLS-DA showed group separation, with component 5 having high predictive power (Q2=0.68345).
- Significant differences in metabolite abundance were observed: 12 more abundant in controls, 3 in the DENV-infected group.
- ROC analysis indicated high sensitivity (80% for m/z 246.265, 96% for a set of metabolites).
- Identified metabolites belong to sphingolipids, fatty acids, glycerol lipids, and sterols.
Conclusions:
- Dengue virus infection induces significant alterations in lipid metabolism in pediatric patients.
- Specific lipid metabolites show potential as diagnostic biomarkers for dengue fever.
- Lipidomics is a valuable tool for dengue research and biomarker discovery, aiding in the development of diagnostic tools.
Abstract:
Dengue fever is a significant global health concern, particularly in tropical and subtropical regions, where it disproportionately affects children and adolescents. The disease, caused by the dengue virus (DENV), triggers a complex immune response that leads to metabolic alterations, particularly in lipid metabolism, which plays a key role in inflammation and disease progression. Despite advancements in diagnostic methods, the search for novel biomarkers may support the development of new diagnostic tools for faster patient screening. In this study, we applied a lipidomics approach using liquid chromatography-mass spectrometry to analyze the serum lipid metabolome of children and adolescents infected with DENV (n = 25) compared to controls (n = 15). Multivariate statistics included partial least squares discriminant analysis (PLS-DA) to assess group separation and receiver operating characteristic (ROC) curve analysis to evaluate biomarker performance. The PLS-DA revealed a tendency of separation between groups, with component 5 showing the highest predictive power (Q2 = 0.68345). From this data, 12 metabolites were significantly more abundant in controls, while 3 were more abundant in DENV infected group. ROC curve analysis demonstrated a sensitivity of 80% for the metabolite of m/z 246.265, and a sensitivity of 96% for all metabolites, as a set. The metabolites were attributed to sphingolipids, fatty acids, glycerol lipids, and sterols. Our findings reveal significant lipid metabolic alterations in pediatric dengue fever, highlighting their biomarker potential. This study reinforces the value of lipidomics in dengue research and biomarker discovery, which may contribute to the development of diagnosis tools that will improve patient care.
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