Glycated Hemoglobin Measurement and Prediction of CVD

Editor's Note: Commentary based on Emerging Risk Factors Collaboration, Di Angelantonio E, Gao P, Khan H, et al. Glycated hemoglobin measurement and prediction of cardiovascular disease. JAMA 2014;311:1225-33.


It is well known that a diagnosis of diabetes confers higher risk of future cardiovascular disease (CVD). However, the value of measuring levels of glycated hemoglobin (HbA1c) for the prediction of cardiovascular events in the absence of diabetes is indeterminate. Previously, a UK population-based prospective cohort (European Prospective Investigation of Cancer [EPIC]-Norfolk) in which only 2.8% had diabetes showed that addition of HbA1c made a small but statistically significant improvement to discrimination in men but not in women, without significant improvement in reclassification of risk category.1

To further address this question, the Emerging Risk Factors Collaboration studied a much larger combined population. They asked whether or not the addition of HbA1c values to conventional cardiovascular risk factors was associated with improvement in prediction of CVD risk among those without diabetes.


The study was a pooled analyses of 73 prospective studies involving 294,998 participants without a known history of diabetes mellitus or CVD at baseline assessment. The authors performed a rigorous statistical analysis to see if HbA1c provided any additional predictive information to the established CVD risk factors.2 Hazard ratios were calculated using Cox proportional hazard regression models. Further, authors developed CVD risk prediction models containing important conventional risk factors (i.e., age, sex, smoking status, systolic blood pressure, and total and high-density lipoprotein [HDL] cholesterol) without or with a measure of glycemia, and calculated improvements in predictive ability using measures of risk discrimination (e.g., C-statistic) and reclassification (e.g., net reclassification improvement) of participants across predicted 10-year risk categories of low (<5%), intermediate (5% to <7.5%), and high (≥7.5%) risk.

The authors performed rigorous and extensive analyses as evident from supplemental information including nine tables and 23 figures in the publication.


The study had a long median follow-up of 9.9 years. Overall, the mean age of participants at baseline was 58 years, 49% were women, and 86% lived in Europe or North America. Mean level of HbA1c was 5.37%, not unexpected given this was a population without diabetes. Fatal and nonfatal CVD outcomes included 13,237 events of coronary heart disease and 7,603 incidents of stroke.

Interestingly, there was an approximately J-shaped association between HbA1c values and CVD risk, with slight attenuation after adjustment for HDL cholesterol and C-reactive protein. The addition of HbA1c to the CVD risk prediction model containing conventional cardiovascular risk factors led to a very slight C-index change of 0.0018 (0.0003 to 0.0033) and a statistically non-significant net reclassification improvement of 0.42% (–0.63 to 1.48%) for the categories of predicted 10-year CVD risk.


In a study of individuals without known CVD or diabetes, additional assessment of HbA1c values provided little incremental benefit for prediction of CVD risk.


The Emerging Risk Factors Collaboration continues to provide valuable insights to the questions pertaining to the field of cardiometabolic disease. Previously they have shown that diabetes confers about a two-fold excess risk for a wide range of vascular diseases, independently from other conventional risk factors.3 They also showed that in addition to vascular disease, diabetes is associated with substantial premature death from cancers, infectious diseases, external causes, and degenerative disorders, independent of several major risk factors.4

Regarding the role of HbA1c in the prediction of future CVD, currently the Reynolds Risk Score incorporates information on HbA1c, although only for use in people known to have diabetes.5 The present study comprehensively showed that among individuals without diabetes, HbA1c values provide little incremental benefit for prediction of CVD risk.

Further, a comparison of glycemia measures to predict first-onset CVD outcomes suggested that the improvement provided by HbA1c assessment in prediction of CVD risk was similar to improvements estimated for assessment of fasting, random, or postload plasma glucose levels. These finding are a validation of the 2013 American College of Cardiology/American Heart Association Guideline on the Assessment of Cardiovascular Risk that do not endorse measurement of glycemia measures for assessment of future CVD risk.6


  1. Simmons RK, Sharp S, Boekholdt SM, et al. Evaluation of the Framingham risk score in the European Prospective Investigation of Cancer-Norfolk cohort: does adding glycated hemoglobin improve the prediction of coronary heart disease events? Arch Intern Med 2008;168:1209-16.
  2. Hlatky MA. Framework for evaluating novel risk markers. Ann Intern Med 2012;156:468-9.
  3. Emerging Risk Factors Collaboration, Sarwar N, Gao P, Seshasai SR, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet 2010;375:2215-22.
  4. Emerging Risk Factors Collaboration, Seshasai SR, Kaptoge S, Thompson A, et al. Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med 2011;364:829-41.
  5. Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA 2007;297:611-619.
  6. Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2014;63(25 Pt B):2935-59.

Keywords: Cardiovascular Diseases, Diabetes Mellitus, Hemoglobin A, Glycosylated, Mitogen-Activated Protein Kinases, Neoplasms

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