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Human metabolic phenotype diversity and its association with diet and blood pressure

Abstract

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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Figure 1: Hierarchical cluster analysis using group average linkage based on median 1 H NMR urine spectra, by population sample and gender ( n = 4,630).
Figure 2: Plots of cross-validated principal components analysis scores ( n = 4,630).
Figure 3: O-PLS-DA scores and loadings plots (bootstrap analyses) for participants reporting high vegetable/low animal protein and low vegetable/high animal protein intakes, first 24-h urinary specimens.

References

  1. 1

    Dumas, M. E. et al. Assessment of analytical reproducibility of 1H NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP study. Anal. Chem. 78, 2199–2208 (2006)

    CAS  Article  Google Scholar 

  2. 2

    Nicholson, J. K., Holmes, E. & Wilson, I. D. Gut microorganisms, mammalian metabolism and personalized health care. Nature Rev. Microbiol. 3, 431–438 (2005)

    CAS  Article  Google Scholar 

  3. 3

    Sabeti, P. C. et al. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913–918 (2007)

    CAS  ADS  Article  Google Scholar 

  4. 4

    Murray, C. J. & Lopez, A. D. Mortality by cause for eight regions of the world: global burden of disease study. Lancet 349, 1269–1276 (1997)

    CAS  Article  Google Scholar 

  5. 5

    Stamler, J. et al. INTERMAP: Background, aims, design, methods, and descriptive statistics (non-dietary). J. Hum. Hypertens. 17, 591–608 (2003)

    CAS  Article  Google Scholar 

  6. 6

    Elliott, P. et al. Association between protein intake and blood pressure: the INTERMAP study. Arch. Intern. Med. 166, 79–87 (2006)

    CAS  Article  Google Scholar 

  7. 7

    Mulder, T. P., Rietveld, A. G. & van Amelsvoort, J. M. Consumption of both black tea and green tea results in an increase in the excretion of hippuric acid into urine. Am. J. Clin. Nutr. 81 (Suppl.). 256S–260S (2005)

    CAS  Article  Google Scholar 

  8. 8

    Elliott, P. & Stamler, J. in Coronary Heart Disease Epidemiology: From Aetiology to Public Health 2nd edn (eds Marmot, M. & Elliott, P.) 751–768 (Oxford Univ. Press, Oxford, UK, 2005)

    Book  Google Scholar 

  9. 9

    Dieterle, F., Ross, A., Schlotterbeck, G. & Senn, H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal. Chem. 78, 4281–4290 (2006)

    CAS  Article  Google Scholar 

  10. 10

    Hotelling, H. The generalization of Student’s ratio. Ann. Math. Stat. 2, 360–378 (1931)

    Article  Google Scholar 

  11. 11

    Trygg, J. & Wold, S. Orthogonal projections to latent structures (O-PLS). J. Chemometr. 16, 119–128 (2002)

    CAS  Article  Google Scholar 

  12. 12

    Li, M. et al. Symbiotic gut microbes modulate human metabolic phenotypes. Proc. Natl Acad. Sci. USA 105, 2117–2122 (2008)

    CAS  ADS  Article  Google Scholar 

  13. 13

    Chen, S.-H. & Giblett, E. R. Polymorphism of soluble glutamic-pyruvic transaminase: a new genetic marker in man. Science 173, 148–149 (1971)

    CAS  ADS  Article  Google Scholar 

  14. 14

    Lin, S.-H., Lin, Y.-F. & Halperin, M. L. Hypokalaemia and paralysis. Q. J. Med. 94, 133–139 (2001)

    CAS  Article  Google Scholar 

  15. 15

    Intersalt Co-operative Research Group. Intersalt: an international study of electrolyte excretion and blood pressure. Results for 24 hour urinary sodium and potassium excretion. Br. Med. J. 297, 319–328 (1988)

  16. 16

    Elliott, P. et al. Dietary phosphorus and blood pressure. International study of macro- and micro-nutrients and blood pressure. Hypertension 51, 669–675 (2008)

    CAS  Article  Google Scholar 

  17. 17

    Gregory, J. F. et al. Primed, constant infusion with 2H3 serine allows in vivo kinetic measurement of serine turnover, homocysteine remethylation and transsulfuration processes in human one-carbon metabolism. Am. J. Clin. Nutr. 72, 1535–1541 (2000)

    CAS  Article  Google Scholar 

  18. 18

    Samuel, B. S. & Gordon, J. I. A humanized gnotobiotic mouse model of host-archaeal-bacterial mutualism. Proc. Natl Acad. Sci. USA 103, 10011–10016 (2006)

    CAS  ADS  Article  Google Scholar 

  19. 19

    Kahle, K. T. et al. WNK4 regulates apical and basolateral Cl- flux in extrarenal epithelia. Proc. Natl Acad. Sci. USA 101, 2064–2069 (2004)

    CAS  ADS  Article  Google Scholar 

  20. 20

    Elliott, P. et al. Change in salt intake affects blood pressure of chimpanzees: Implications for human populations. Circulation 116, 1563–1568 (2007)

    CAS  Article  Google Scholar 

  21. 21

    Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Human gut microbes associated with obesity. Nature 444, 1022–1023 (2006)

    CAS  ADS  Article  Google Scholar 

  22. 22

    Conlay, L. A., Maher, T. J. & Wurtman, R. J. Alanine increases blood pressure during hypotension. Pharmacol. Toxicol. 66, 415–416 (1990)

    CAS  Article  Google Scholar 

  23. 23

    Holmes, E. et al. Detection of urinary drug metabolite (xenometabolome) signatures in molecular epidemiology studies via statistical total correlation (NMR) spectroscopy. Anal. Chem. 79, 2629–2640 (2007)

    CAS  Article  Google Scholar 

  24. 24

    Grandits, G. A. et al. Method issues in dietary data analysed in the Multiple Risk Factor Intervention Trial. Am. J. Clin. Nutr. 65 (Suppl.). 211S–227S (1997)

    CAS  Article  Google Scholar 

  25. 25

    Wold, S. Cross-validatory estimation of number of components in factor and principal components models. Technometrics 20, 397–405 (1978)

    Article  Google Scholar 

  26. 26

    Martens, H. & Martens, M. Modified jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR). Food Qual. Prefer. 11, 5–16 (2000)

    Article  Google Scholar 

  27. 27

    Crockford, D. J. et al. Curve fitting method for direct quantitation of compounds in complex biological mixtures using 1H NMR: Application in metabonomic toxicology studies. Anal. Chem. 77, 4556–4562 (2005)

    CAS  Article  Google Scholar 

  28. 28

    Fekkes, D., Voskuilen-Kooyman, A., Jankie, R. & Huijmans, J. Precise analysis of primary amino acids in urine by an automated high-performance liquid chromatography method: Comparison with ion-exchange chromatography. J. Chromatogr. B 744, 183–188 (2000)

    CAS  Article  Google Scholar 

  29. 29

    Lenz, E. M & Wilson, I.D. Analytical strategies in metabonomics. J. Proteome Res. 443, 443–458 (2007)

    Article  Google Scholar 

  30. 30

    Cloarec, O. et al. Statistical total correlation spectroscopy: An exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Anal. Chem. 77, 1282–1289 (2005)

    CAS  Article  Google Scholar 

Download references

Acknowledgements

INTERMAP is supported by the US National Heart, Lung, and Blood Institute (RO1 HL50490 and RO1 HL084228); the Chicago Health Research Foundation; and national agencies in Japan (the Ministry of Education, Science, Sports, and Culture), China and the UK. The funders had no role in the design and conduct of the study, or in the collection, management, analysis and interpretation of the data, or in the preparation, review or approval of the manuscript. The INTERMAP study has been accomplished through the work of the staff at the local, national and international centres. A partial listing of colleagues is in ref. 5. We thank M. Rantalanein, O. Cloarec, E. Want and O. Beckonert (Imperial College London) for their assistance with the statistical and NMR analyses; and P. Oefner and H. Kaspar (University of Regensburg) for gas chromatography mass spectrometry analyses.

Author Contributions The INTERMAP study was conceived by J.S., P.E. and Rose Stamler (deceased); INTERMAP urinary amino acids study was by J.S., P.E., M.L.D. and H.K.; INTERMAP metabonomics study was by J.K.N. and P.E., with E.H., and J.S., M.L.D. The manuscript was written by P.E., J.K.N., E.H. and J.S.; analyses were done by R.L.L., M.B., I.K.S.Y., Q.C. and I.J.B. T.E., M.D.I., and K.V. provided statistical and analytical support. H.U. and L.Z. were responsible for data collection. All authors reviewed and approved the manuscript.

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Correspondence to Jeremy K. Nicholson or Paul Elliott.

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Holmes, E., Loo, R., Stamler, J. et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 453, 396–400 (2008). https://doi.org/10.1038/nature06882

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