Résumé
Diabetes is a leading health problem in the developed world. The recent surge of wealth in Qatar has made it one of the most vulnerable nations to diabetes and related diseases. Recent technological advances in 1H nuclear magnetic resonance (NMR) spectroscopy techniques for metabolomics profiling offer a great opportunity for biomarkers discovery to better understand the disease. Using this technology, we present in this study, an integrative approach with a newly proposed algorithm named Kernel Spectral Clustering (KSC) to discover new metabolites and possibly new biomarkers. We performed an integrative analysis of 1H NMR spectras measured in urine, from 348 participants of the Qatar Metabolomics Study on Diabetes (QMDiab). Our analyses revealed grouped metabolites that correlate with diabetes and identified specific metabolites affected by antidiabetes medication, which constraints differentiation between diabetic and control patients.