Читать книгу Diabetic Retinopathy and Cardiovascular Disease - Группа авторов - Страница 24

Overall, risk prediction models based on traditional cardiovascular risk factors have limited ability to predict outcomes in patients with type 2 diabetes. These risk scores typically include minimal biomarker data, often just a lipid marker. More comprehensive scores may include albuminuria, HbA1c or eGFR. It has been hypothesised that novel biomarkers may exist which could improve cardiovascular risk prediction. Selection of candidate biomarkers has largely been hypothesis-driven, based on known pathophysiological pathways such as cardiac stress, inflammation, matrix remodelling and advanced glycation end products [43]. Numerous individual biomarkers have been shown to be associated with cardiovascular outcomes in patients with diabetes, although the strength of the association and degree of risk inferred is often limited. It has been proposed than inclusion of multiple biomarkers, relating to various pathophysiological mechanisms, may improve risk prediction models. The benefit of including biomarkers in a risk prediction tool can be evaluated by assessing discrimination, calibration and reclassification. Discrimination and calibration are discussed earlier in the chapter. Reclassification is the ability of a particular variable to improve an individuals’ calculated levels of risk beyond the defined thresholds such that their determined risk categories change, thus having an impact on the recommended clinical management.

Aside from established biomarkers, such as lipids, eGFR, urine albumin-creatinine ratio and HbA1c, current guidelines do not recommend the routine clinical use of any novel biomarkers [27, 28]. Some of the most well investigated novel biomarkers include N-terminal pro-B-type natriuretic peptide, high-sensitivity troponin T and high-sensitivity C-reactive protein (hsCRP) [43]. Independent associations with cardiovascular outcomes in patients with diabetes have been established for each of these [44–47]. N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T are both markers of cardiac stress and high-sensitivity C-reactive protein is a marker of inflammation.

A few studies have investigated the predicative capabilities of multiple novel biomarkers beyond established risk factors among patients with diabetes [43]. These studies have investigated between 23 and 237 different biomarkers with final models including between 3 and 10 biomarkers [48–50]. Each of these studies showed modest improvements in discrimination and reclassification with the addition of the novel biomarkers to a model based on traditional cardiovascular risk factors. However, it is not known whether these small differences in predictive capability would lead to any meaningful changes in clinical management or to prevention of subsequent cardiovascular outcomes, let alone whether the measurement of multiple biomarkers could be cost-effective.

Most studies to date have looked at biomarkers that relate to known pathophysiological mechanisms of cardiovascular disease. Thus there is a reasonable likelihood that these biomarkers will correlate with known risk factors and provide limited additional predictive capability. Recent developments in approaches to the discovery of novel biomarkers, such as proteomics and metabolomics, can assess hundreds or thousands of potential biomarkers simultaneously. It remains to be seen whether such studies could yield useful biomarkers for predicting cardiovascular disease in patients with diabetes.