Skip to main content
SpringerLink
Log in

Better risk assessment with glycated hemoglobin instead of cholesterol in CVD risk prediction charts

  • PREDICTION
  • Published:
European Journal of Epidemiology Aims and scope Submit manuscript

Abstract

Traditional risk charts for the prediction of cardiovascular disease (CVD) include cholesterol parameters. We evaluated how models predict fatal CVD when cholesterol is replaced by glucose parameters. We used data from NHANES III, a US survey conducted 1988–1994 (follow-up until 2006); 15,454 participants (1,716 CVD deaths) were included. Based on the ESC SCORE method, we used age, sex, blood pressure, smoking and either of the following: (1) total cholesterol, (2) total-to-HDL-cholesterol, (3) glucose, (4) glycated hemoglobin (A1C). Scaled Brier score (BS), Nagelkerke’s R2 (NR) and integrated discrimination improvement (IDI) were used for model comparison. The ranking (best to worst) was: A1C (BS = 11.62 %; NR = 0.0865; IDI = 0.0091), glucose (11.16 %; 0.0734; 0.0067), total-to-HDL-cholesterol (9.97 %; 0.0547; 0.0010), cholesterol (9.75 %; 0.0484; 0, reference). Differences between models with cholesterol and glucose or A1C were statistically significant. This study suggests the use of A1C instead of cholesterol parameters in charts to assess CVD risk.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Abbreviations

CVD:

Cardiovascular disease

A1C:

Glycated hemoglobin

NHANES:

National Health and Nutrition Examination Survey

References

  1. Conroy RM, Pyorala K, Fitzgerald AP, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987–1003.

    Article  PubMed  CAS  Google Scholar 

  2. Assmann G, Cullen P, Schulte H. Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular munster (PROCAM) study. Circulation. 2002;105(3):310–5.

    Article  PubMed  Google Scholar 

  3. D’Agostino RB Sr, Grundy S, Sullivan LM, Wilson P. Validation of the framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001;286(2):180–7.

    Article  PubMed  Google Scholar 

  4. Faeh D, Braun J, Bopp M. Body mass index vs cholesterol in cardiovascular disease risk prediction models. Arch Intern Med. 2012;172(22):1766–8.

    Article  PubMed  Google Scholar 

  5. Selvin E, Steffes MW, Zhu H, et al. Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. N Engl J Med. 2010;362(9):800–11.

    Article  PubMed  CAS  Google Scholar 

  6. National Center for Health Statistics. Plan and operation of the Third National Health and Nutrition Examination Survey, 1988–94. Series 1: programs and collection procedures. Vital Health Stat 1. 1994(32):1–407.

  7. Royston P, Explained variation for survival models. Stata J (2006) 2006; 6(1):83–96.

    Google Scholar 

  8. Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128–38.

    Article  PubMed  Google Scholar 

  9. Gneiting T, Raftery AE. Strictly proper scoring rules, prediction and estimation. J Am Stat Assoc. 2007;102:359–78.

    Article  CAS  Google Scholar 

  10. Nishimura R, Nakagami T, Sone H, Ohashi Y, Tajima N. Relationship between hemoglobin A1c and cardiovascular disease in mild-to-moderate hypercholesterolemic Japanese individuals: subanalysis of a large-scale randomized controlled trial. Cardiovasc Diabetol. 2011;10:58.

    Article  PubMed  CAS  Google Scholar 

  11. Orchard TJ, Temprosa M, Goldberg R, et al. The effect of metformin and intensive lifestyle intervention on the metabolic syndrome: the diabetes prevention program randomized trial. Ann Intern Med. 2005;142(8):611–9.

    Article  PubMed  CAS  Google Scholar 

  12. Iqbal N, Rubenstein AH. Does lowering of blood glucose improve cardiovascular morbidity and mortality? Clin J Am Soc Nephrol. 2008;3(1):163–7.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by the Swiss National Science Foundation (grants 3347CO-108806, 33CS30-134273, 32473B-125710, 32473B-143897).

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. Institute of Social and Preventive Medicine (ISPM), University of Zurich, Hirschengraben 84, 8001, Zurich, Switzerland

    David Faeh, Sabine Rohrmann & Julia Braun

Authors
  1. David Faeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sabine Rohrmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Julia Braun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Faeh.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 138 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Faeh, D., Rohrmann, S. & Braun, J. Better risk assessment with glycated hemoglobin instead of cholesterol in CVD risk prediction charts. Eur J Epidemiol 28, 551–555 (2013). https://doi.org/10.1007/s10654-013-9827-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10654-013-9827-6

Keywords

Search

Navigation