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"We've been doing toxicological and disease diagnostics based on metabolic profiling for more than 20 years. That's before genomics or proteomics raised their ugly heads. Author believes that metabonomics is "more closely related to things in the clinical world" than either genomics or proteomics, owing to the fact that metabonomic signatures reflect both genetic information and environmental influences. THE VALUE OF genomic and proteomic measurements, is "considerably more limited than most people think." For example, changes in gene and protein expression needn't result in an "endpoint change." That is, the change in one gene or protein could be compensated elsewhere, resulting in no net change. "That's always the big problem with genes and proteins. "Their up or down regulation can be part of the overall homeostatic or corrective process of the cell, not necessarily part of the pathology." Author suspects that most diseases have a metabolic signature at some level. The challenge is finding that signature. Finding the right matrix is important, whether it be urine, blood, cerebrospinal fluid, or solid tissue. "Urine carries information on almost everything, because [the kidney is] your ultimate excretory organ, where homeostasis is maintained. "There's a tremendous amount of information that can be obtained from urine, if you can analyze all the thousands of metabolites that are in there."
Metabonomics experiments are carried out by analyzing biological fluids or tissue extracts with techniques--such as nuclear magnetic resonance spectroscopy, mass spectrometry, or infrared spectroscopy--that provide many data points simultaneously. Even intact tissue samples taken during biopsies can be analyzed, using the NMR technique known as magic angle spinning.The metabonomic profile is dominated by molecules smaller than 1,000 daltons. That molecular weight range "incorporates pretty much all energy pathways, all catabolic pathways, and many biosynthetic pathways. Authors focus on NMR measurements. The subtle differences in NMR spectra are practically impossible to identify just by visual inspection. Data mining and statistical techniques must be used to pull out what authors calls "latent diagnostic information."
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