C‐reactive protein concentration predicts mortality in type 2 diabetes: the Diabetes Heart Study

Abstract

Diabet. Med. 29, 767–770 (2012) Abstract Aims Although current American Heart Association guidelines address C‐reactive protein concentration and cardiovascular disease risk, it remains unclear whether this paradigm is consistent across populations with differing disease burdens. Individuals with Type 2 diabetes mellitus represent one group at increased risk of cardiovascular disease and subsequent mortality. This study aimed to examine the relationship between C‐reactive protein concentrations and risk for all‐cause mortality in European Americans with Type 2 diabetes from the Diabetes Heart Study. Methods A total of 846 European Americans with Type 2 diabetes and baseline measures of C‐reactive protein were evaluated. Vital status was determined after a follow‐up period of 7.3 ± 2.1 years (mean ± SD). C‐reactive protein concentrations were compared between living and deceased subgroups along with other known risk factors for cardiovascular disease, including blood lipids. Logistic regression was performed to determine risk for mortality associated with increasing C‐reactive protein concentrations. Results At follow‐up 160 individuals (18.7%) were deceased. No significant differences in baseline serum glucose or lipid measures were observed between living and deceased subgroups. Baseline C‐reactive protein concentrations were significantly higher in the deceased subgroup (9.37 ± 15.94) compared with the living subgroup (5.36 ± 7.91 mg/l; P < 0.0001). Participants with C‐reactive protein concentrations of 3–10 mg/l were approximately two times more likely to be deceased at follow‐up (OR 2.06; 95% CI 1.17–3.62); those with C‐reactive protein >10 mg/l were more than five times more likely to be deceased (OR 5.24; CI 2.80–9.38). Conclusions This study documents the utility of C‐reactive protein in predicting risk for all‐cause mortality in European Americans with Type 2 diabetes and supports its use as a screening tool in risk prediction models.