Physicians' Academy for Cardiovascular Education

Common CIMT does not improve risk assessment in patients with diabetes

Literature - den Ruijter HM, Peters SA, Groenewegen KA, et al. - Diabetologia. 2013 Apr 9. [Epub ahead of print]


Common carotid intima-media thickness does not add to Framingham risk score in individuals with diabetes mellitus: the USE-IMT initiative.

 
den Ruijter HM, Peters SA, Groenewegen KA, et al.
Diabetologia. 2013 Apr 9. [Epub ahead of print]
 

Background

Individuals with diabetes are at a twofold higher risk of cardiovascular morbidity and mortality than individuals with normal glucose metabolism [1,2]. Guidelines recommend assessing the absolute risk of developing cardiovascular disease (CVD) to appropriately treat those at high-risk. Only few prediction models have specifically been developed for individuals with diabetes or include a plasma glucose measure [3, 4]. Most risk prediction models have not been validated and tested for their predictive accuracy, but the Framingham risk prediction model forms an exception [4,5].
Since accelerated atherogenesis is seen in individuals with diabetes, including a measure of preclinical atherosclerosis may improve risk prediction. It has been proposed to add measurement of carotid intima media thickness (CIMT) to cardiovascular risk factors, the value of which has not been studied in specific high-risk groups.
This study therefore set out to assess whether measurement of mean common CIMT, next to Framingham risk score, improves CV risk prediction in individuals with diabetes, without pre-existing CVD. The USE-IMT, an ongoing global individual participant data meta-analysis project based on prospective cohort studies was used [6]. Data of 4220 individuals with diabetes were used for his analysis, and compared to data of 56194 individuals of the general USE-IMT population.
 

Main results

  • Classical cardiovascular risk scores (used in Framingham model) were strongly related with the occurrence of first-time stroke or myocardial infarction, both in the general USE-IMT group and in the diabetes group. However, the relationships between smoking, blood pressure, age or sex with CV events were weaker in the diabetic group.
  • The association of mean common CIMT and event rate was similar for the general USE-IMT group and the diabetic group.
  • Individuals with diabetes were distributed into different risk score categories, based on the Framingham model. When mean common CIMT was added to the risk prediction model, 85% of the individuals remained in the same risk category. The majority of people who did not experience an event were correctly classified into lower-risk categories. Of individuals that did go through an event, a similar number was correctly up-classified as incorrectly down-classified.
  • The added value of mean common CIMT was small and non-significant, for both men and women. The integrated discrimination improvement was 0.005195% (95%CI: 0.0011-0.0091) for individuals with diabetes (both men and women).
 

Conclusion

Measurement of mean common CIMT, in addition to Framingham risk factors, does not provide additional benefit in CVD risk assessment in individuals with diabetes. The Framingham risk score is widely used and well validated, but room for improvement exists, for instance with regard to subclinical atherosclerosis. While common CIMT is related to the risk of coronary events or CVD, it has no added value in individual risk stratification in clinical practice.
 

References

1. Asia Pacific Cohort Studies Collaboration (2003) The effects of diabetes on the risks of major cardiovascular diseases and death in the Asia-Pacific region. Diabetes Care 26:360–366
2. The Emerging Risk Factors Collaboration (2010) Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet 375:2215–2222
3. Chamnan P, Simmons RK, Sharp SJ, Griffin SJ, Wareham NJ (2009) Cardiovascular risk assessment scores for people with diabetes: a systematic review. Diabetologia 52:2001–2014
4. van Dieren S, Beulens JWJ, Kengne AP et al (2011) Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review. Heart 98:360–369
5. D’Agostino RB Sr, Vasan RS, Pencina MJ et al (2008) General cardiovascular risk profile for use in primary care: The Framingham Heart Study. Circulation 117:743–753
 
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