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Journal of biomolecular NMR, (20 Jun 2008)
Concepts in Magnetic Resonance Part B: Magnetic Resonance Engineering 19B (1), 35-43 (2003)
Proteins Structure Function and Bioinformatics 63 (1), 210 (2006)
Proteins Structure Function and Bioinformatics 59 (4), 655 (2005)
Journal of Computer-Aided Molecular Design 20 (5), 263 (2006)
Journal of Biomolecular NMR 8 (4), (1996)
Journal of Biomolecular NMR 4 (1), (1994)
Nature, (20 Apr 2008)
Metabolic phenotypes are the products of interactions among a variety of factors—dietary, other lifestyle/environmental, gut microbial and genetic1, 2, 3. We use a large-scale exploratory analytical approach to investigate metabolic phenotype variation across and within four human populations, based on 1H NMR spectroscopy. Metabolites discriminating across populations are then linked to data for individuals on blood pressure, a major risk factor for coronary heart disease and stroke (leading causes of mortality worldwide4). We analyse spectra from two 24-hour urine specimens for each of 4,630 participants from the INTERMAP epidemiological study5, involving 17 population samples aged 40–59 in China, Japan, UK and USA. We show that urinary metabolite excretion patterns for East Asian and western population samples, with contrasting diets, diet-related major risk factors, and coronary heart disease/stroke rates, are significantly differentiated (P < 10-16), as are Chinese/Japanese metabolic phenotypes, and subgroups with differences in dietary vegetable/animal protein and blood pressure6. Among discriminatory metabolites, we quantify four and show association (P < 0.05 to P < 0.0001) of mean 24-hour urinary formate excretion with blood pressure in multiple regression analyses for individuals. Mean 24-hour urinary excretion of alanine (direct) and hippurate (inverse), reflecting diet and gut microbial activities2, 7, are also associated with blood pressure of individuals. Metabolic phenotyping applied to high-quality epidemiological data offers the potential to develop an area of aetiopathogenetic knowledge involving discovery of novel biomarkers related to cardiovascular disease risk.
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