Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics

A Corrigendum to this article was published on 01 April 2003


Although a wide range of risk factors for coronary heart disease have been identified from population studies, these measures, singly or in combination, are insufficiently powerful to provide a reliable, noninvasive diagnosis of the presence of coronary heart disease. Here we show that pattern-recognition techniques applied to proton nuclear magnetic resonance (1H-NMR) spectra of human serum can correctly diagnose not only the presence, but also the severity, of coronary heart disease. Application of supervised partial least squares-discriminant analysis to orthogonal signal-corrected data sets allows >90% of subjects with stenosis of all three major coronary vessels to be distinguished from subjects with angiographically normal coronary arteries, with a specificity of >90%. Our studies show for the first time a technique capable of providing an accurate, noninvasive and rapid diagnosis of coronary heart disease that can be used clinically, either in population screening or to allow effective targeting of treatments such as statins.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Comparison of patients with severe atherosclerosis (TVD) and patients with normal coronary arteries (NCA).
Figure 2: Prediction of coronary artery status using the PLS-DA model.
Figure 3: Comparison of patients with different severity of coronary atherosclerosis.


  1. National Statistics Series DH1 no. 31, Mortality Statistics (1998). The Stationery Office (UK Government), London, England.

  2. Kjelsberg, M.O., Cutler, J.A. & Dolecek, T.A. Brief description of the Multiple Risk Factor Intervention Trial. Am. J. Clin. Nutr. 65 (Suppl. 1), 191S–195S (1997).

    CAS  Article  Google Scholar 

  3. Kuller, L.H. et al. Cigarette smoking and mortality. MRFIT Research Group. Prev. Med. 20, 638–654 (1991).

    CAS  Article  Google Scholar 

  4. Multiple Risk Factor Intervention Trial Research Group. Relationship between baseline risk factors and coronary heart disease and total mortality in the Multiple Risk Factor Intervention Trial. Prev. Med. 15, 254–273 (1986).

  5. McIlvain, H.E., McKinney, M.E., Thompson, A.V. & Todd, G.L. Application of the MRFIT smoking cessation program to a healthy, mixed-sex sample. Am. J. Prev. Med. 8, 165–170 (1992).

    CAS  Article  Google Scholar 

  6. Ross, R. Atherosclerosis—an inflammatory disease. N. Engl. J. Med. 340, 115–126 (1999).

    CAS  Article  Google Scholar 

  7. Cullen, P., Funke, H., Schulte, H. & Assmann, G. Lipoproteins and cardiovascular risk—from genetics to CHD prevention. Eur. Heart J. 19 (Suppl. C), C5–C11 (1998).

    PubMed  Google Scholar 

  8. Isles, C.G. & Paterson, J.R. Identifying patients at risk for coronary heart disease: Implications from trials of lipid-lowering drug therapy. Q.J. Med. 93, 567–574 (2000).

    CAS  Article  Google Scholar 

  9. Nicholson, J.K., Lindon, J.C. & Holmes, E. 'Metabonomics': Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29, 1181–1189 (1999).

    CAS  Article  Google Scholar 

  10. Nicholson, J.K. & Wilson, I.D. High resolution proton magnetic resonance spectroscopy of biological fluids Prog. Nucl. Magn. Reson. Spectrosc. 21, 449–501 (1989).

    CAS  Article  Google Scholar 

  11. Lindon, J.C., Holmes, E. & Nicholson, J.K. Pattern recognition methods and applications in biomedical magnetic resonance. Prog. Nucl. Magn. Reson. Spectrosc. 39, 1–40 (2001).

    CAS  Article  Google Scholar 

  12. Lindon, J.C., Nicholson, J.K.N., Holmes, E. & Everett, J.R. Metabonomics: metabolic processes studied by NMR spectroscopy of biofluids. Concepts Magn. Reson. 12, 289–320 (2000).

    CAS  Article  Google Scholar 

  13. Holmes, E. et al. Chemometric models for toxicity classification based on NMR spectra of biofluids. Chem. Res. Toxicol. 13, 471–478 (1999).

    Article  Google Scholar 

  14. Storck, T., von Brevern, M.C., Behrens, C.K., Scheel, J. & Bach, A. Transcriptomics in predictive toxicology. Curr. Opin. Drug Discov. Devel. 5, 90–97 (2002).

    CAS  PubMed  Google Scholar 

  15. Nicholson, J.K., Foxall, P.J., Spraul, M., Farrant, D.R. & Lindon, J.C. 750 MHz 1H and 1H-13C NMR spectroscopy of human blood plasma. Anal. Chem. 67, 793–811 (1995).

    CAS  Article  Google Scholar 

  16. Ala-Korpela, M. 1H-NMR spectroscopy of human blood plasma. Prog. Nucl. Magn. Reson. Spectrosc. 27, 475–554 (1995).

    CAS  Article  Google Scholar 

  17. Eriksson, L., Johansson, E., Kettanah-Wold, N. & Wold, S. Introduction to Multi and Megavariate Data Analysis Using Projection Methods (PCA and PLS-DA) (Umetrics AB, Malmo, Sweden, 1999).

    Google Scholar 

  18. Wold, S., Antti, H., Lindgren, F. & Ohman, J. Orthogonal signal correction of near-infrared spectra. Chemometrics Intelligent Lab. Systems 44, 175–185 (1998).

    CAS  Article  Google Scholar 

  19. Ala-Korpela, M., Hiltunen, Y. & Bell, J.D. Quantification of biomedical NMR data using artificial neural network analysis: Lipoprotein lipid profiles from 1H NMR data of human plasma. NMR Biomed. 8, 235–244 (1995).

    CAS  Article  Google Scholar 

  20. Otvos, J. In Handbook of Lipoprotein Testing (eds. Rifai, N., Warnick, R. & Dominiczak, M.) 497–508 (AACC Press, Washington DC, 1997).

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to David J. Grainger.

Ethics declarations

Competing interests

E.H., J.K.N and D.J.G. declare financial interest in this publication. These authors are named inventors on one or more patents filed partly as a result of the work described in this report, and these patents are assigned to Metabometrix Limited, a company in which E.H. and J.K.N. hold more than 5% equity stakes.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Brindle, J., Antti, H., Holmes, E. et al. Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat Med 8, 1439–1445 (2002).

Download citation

  • Published:

  • Issue Date:

  • DOI:

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing