Abstract
Background
In western populations, high-sensitivity C-reactive protein (hsCRP), and to a lesser degree serum creatinine and haemoglobin A1c, predict risk of coronary heart disease (CHD). However, data on Asian populations that are increasingly affected by CHD are sparse and it is not clear whether these biomarkers can be used to improve CHD risk classification.
Design and methods
We conducted a nested case–control study within the Singapore Chinese Health Study cohort, with incident ‘hard’ CHD (myocardial infarction or CHD death) as an outcome. We used data from 965 men (298 cases, 667 controls) and 528 women (143 cases, 385 controls) to examine the utility of hsCRP, serum creatinine and haemoglobin A1c in improving the prediction of CHD risk over and above traditional risk factors for CHD included in the ATP III model. For each sex, the performance of models with only traditional risk factors used in the ATP III model was compared with models with the biomarkers added using weighted Cox proportional hazards analysis. The impact of adding these biomarkers was assessed using the net reclassification improvement index.
Results
For men, loge hsCRP (hazard ratio 1.25, 95% confidence interval: 1.05; 1.49) and loge serum creatinine (hazard ratio 4.82, 95% confidence interval: 2.10; 11.04) showed statistically significantly associations with CHD risk when added to the ATP III model. We did not observe a significant association between loge haemoglobin A1c and CHD risk (hazard ratio 1.83, 95% confidence interval: 0.21; 16.06). Adding hsCRP and serum creatinine to the ATP III model improved risk classification in men with a net gain of 6.3% of cases (p-value = 0.001) being reclassified to a higher risk category, while it did not significantly reduce the accuracy of classification for non-cases. For women, squared hsCRP was borderline significantly (hazard ratio 1.01, 95% confidence interval: 1.00; 1.03) and squared serum creatinine was significantly (hazard ratio 1.81, 95% confidence interval: 1.49; 2.21) associated with CHD risk. However, the association between squared haemoglobin A1c and CHD risk was not significant (hazard ratio 1.05, 95% confidence interval: 0.99; 1.12). The addition of hsCRP and serum creatinine to the ATP III model resulted in 3.7% of future cases being reclassified to a higher risk category (p-value = 0.025), while it did not significantly reduce the accuracy of classification for non-cases.
Conclusion
Adding hsCRP and serum creatinine, but not haemoglobin A1c, to traditional risk factors improved CHD risk prediction among non-diabetic Singaporean Chinese. The improved risk estimates will allow better identification of individuals at high risk of CHD than existing risk calculators such as the ATP III model.
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References
Supplementary Material
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