Physicians' Academy for Cardiovascular Education

Adding HbA1c to conventional CVD risk factors hardly improves prediction of incident CVD

Literature - The Emerging Risk Factors Collaboration - JAMA. 2014;311(12):1225-1233


Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease

The Emerging Risk Factors Collaboration
JAMA. 2014;311(12):1225-1233. doi:10.1001/jama.2014.1873


Screening for diabetes mellitus is recommended to be performed by assessing glycaemia measures, such as fasting blood glucose levels and levels of glycated haemoglobin (HbA1c), in order to lower the incidence of diabetes-specific microvascular complications [1,2]. Higher levels of glycaemia have also been associated with higher incidence of cardiovascular disease (CVD), thus it has been proposed that glycaemia measures may also be used to better predict CVD risk [3-5].
Several guidelines have now included measurement of HbA1c and/or fasting glucose in CVD risk assessment strategies [6-8]. Glycaemia measures were, however, not included in the American College of Cardiology/American Heart Association guideline on the assessment of CV risk [9].
This study evaluated whether adding information on HbA1c levels to prognostic models containing conventional CV risk factors improves prediction of first-onset CVD outcomes in middle-aged and older adults without a known history of diabetes. Data of 294998 participants enrolled in prospective cohorts of the Emerging Risk Factors Collaboration were used.

Main results

  • During a median follow-up of 9.9 (IQR: 7.6-13.2) years, 20840 incident fatal and nonfatal CVD outcomes were documented.  
  • Approximate J-shaped associations were seen between HbA1c, fasting glucose, random glucose or postload glucose and CVD risk, when adjusted for several conventional CVD risk factors.
  • Hazard ratios for CVD changed only slightly after correction for total cholesterol, triglyceride levels, or estimated glomerular filtration rate, but diminished mildly when corrected for HDL-c levels or C-reactive protein concentrations.
  • There were small changes in the C-index and the integrated discrimination index after adding information on HbA1c, fasting glucose, random glucose or postload glucose to CVD risk prediction models that took age, sex, smoking, systolic blood pressure and total and HDL cholesterol levels into account.
  • No significant improvements in net reclassification were seen with the glycaemia measures. No major differences were seen in risk discrimination according to sex or in other clinically relevant subgroups.
  • In participants of whom two or more glycaemia measures were available, change in C-index was broadly similar to the change with either one of the measures.


This analysis of individual data of almost 300000 people without known diabetes and CVD at baseline, shows that information on HbA1c is not associated with clinically meaningful improvement in CVD risk assessment. Only slight improvement of risk discrimination was seen after addition of HbA1c levels to information on conventional CVD risk factors, while no significant improvement in reclassification of participants occurred.
The current observations do not support earlier suggestions that postload glucose levels predict CVD incidence more strongly than other glycaemia measures, since estimated improvements in CVD risk prediction provided by the tested glycaemia measures were similar in this study.
Find this article online


1. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2013;36(suppl 1):S67-S74.
2. Rydén L, Grant PJ, Anker SD, et al; Authors/Task Force Members; ESC Committee for Practice Guidelines (CPG); Document Reviewers. ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD: the Task Force on diabetes, pre-diabetes, and cardiovascular diseases of the European Society of Cardiology (ESC) and developed in collaboration with the European Association for the Study of Diabetes (EASD). Eur Heart J. 2013;34(39):3035-3087.
3. US Preventive Services Task Force. Using nontraditional risk factors in coronary heart disease risk assessment: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2009;151(7):474-482.
4. Schöttker B, Müller H, Rothenbacher D, Brenner H. Fasting plasma glucose and HbA1c in cardiovascular risk prediction: a sex-specific comparison in individuals without diabetes mellitus. Diabetologia. 2013;56(1):92-100.
5. 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(11):1209-1216. 6. Greenland P, Alpert JS, Beller GA, et al; American College of Cardiology Foundation; American Heart Association. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J AmColl Cardiol. 2010;56(25):e50-e103.
7. Anderson TJ, Grégoire J, Hegele RA, et al. 2012 update of the Canadian Cardiovascular Society guidelines for the diagnosis and treatment of dyslipidemia for the prevention of cardiovascular disease in the adult. Can J Cardiol. 2013;29(2):151-167.
8. 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(6):611-619.