| Literature DB >> 19684899 |
Pasi Soininen1, Antti J Kangas, Peter Würtz, Taru Tukiainen, Tuulia Tynkkynen, Reino Laatikainen, Marjo-Riitta Järvelin, Mika Kähönen, Terho Lehtimäki, Jorma Viikari, Olli T Raitakari, Markku J Savolainen, Mika Ala-Korpela.
Abstract
A high-throughput proton (1H) nuclear magnetic resonance (NMR) metabonomics approach is introduced to characterise systemic metabolic phenotypes. The methodology combines two molecular windows that contain the majority of the metabolic information available by 1H NMR from native serum, e.g. serum lipids, lipoprotein subclasses as well as various low-molecular-weight metabolites. The experimentation is robotics-controlled and fully automated with a capacity of about 150-180 samples in 24 h. To the best of our knowledge, the presented set-up is unique in the sense of experimental high-throughput, cost-effectiveness, and automated multi-metabolic data analyses. As an example, we demonstrate that the NMR data as such reveal associations between systemic metabolic phenotypes and the metabolic syndrome (n = 4407). The high-throughput of up to 50,000 serum samples per year is also paving the way for this technology in large-scale clinical and epidemiological studies. In contradiction to single 'biomarkers', the application of this holistic NMR approach and the integrated computational methods provides a data-driven systems biology approach to biomedical research.Entities:
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Year: 2009 PMID: 19684899 DOI: 10.1039/b910205a
Source DB: PubMed Journal: Analyst ISSN: 0003-2654 Impact factor: 4.616