METHODS: The mid-stream urine was collected from 96 patients diagnosed with dengue fever at Penang General Hospital (PGH) and 50 healthy volunteers. Urine samples were analyzed with proton nuclear magnetic resonance (1H NMR) spectroscopy, followed by chemometric multivariate analysis. NMR signals highlighted in the orthogonal partial least square-discriminant analysis (OPLS-DA) S-plots were selected and identified using Human Metabolome Database (HMDB) and Chenomx Profiler. A highly predictive model was constructed from urine profile of dengue infected patients versus healthy individuals with the total R2Y (cum) value 0.935, and the total Q2Y (cum) value 0.832.
RESULTS: Data showed that dengue infection is related to amino acid metabolism, tricarboxylic acid intermediates cycle and β-oxidation of fatty acids. Distinct variations in certain metabolites were recorded in infected patients including amino acids, various organic acids, betaine, valerylglycine, myo-inositol and glycine.
CONCLUSION: Metabolomics approach provides essential insight into host metabolic disturbances following dengue infection.
METHODS: The mid-stream urine collected from both male and female patients diagnosed with dengue fever at Penang General Hospital and fourty-three healthy individuals were analyzed with (1)H NMR spectroscopy, followed by chemometric multivariate analysis. NMR signals which highlighted in the OPLS-DA S-plot were further selected and identified using Human Metabolome Database, Chenomx Profiler.
RESULTS: The results pointed out that NMR urine profiling was able to capture human gender metabolic differences that are important for the distinction of classes of individuals of similar physiological conditions; infected with dengue. Distinct differences between dengue infected patients versus healthy individuals and subtle differences in male versus female infected with dengue were found to be related to the metabolism of amino acid and tricarboxylic acid intermediates cycle.
CONCLUSIONS: The (1)H NMR metabolomic investigation combined with appropriate algorithms and pattern recognition procedures, gave an evidence for the existence of distinct metabolic differentiation of individuals, according to their gender, modulates with the infection risk.