Diabetic Retinopathy and Cardiovascular Disease. Группа авторов
Novel Biomarkers for Cardiovascular Disease Risk
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.
Genetic Risk Scores for Cardiovascular Disease Prediction
The heritability of diabetes and cardiovascular disease risk is polygenic in the vast majority of cases. In recent years, genome-wide association studies conducted in extremely large cohorts have enabled the discovery of single nucleotide polymorphisms (SNPs) that are associated with diabetes and cardiovascular disease. Nevertheless, the degree of risk inferred by each of these polymorphisms in isolation is minimal. Genetic risk scores have been developed, combining multiple SNPs, and have been shown to have strong associations with cardiovascular disease [51]. These associations have been confirmed in a diabetes cohort for a 13-SNP and a 30-SNP risk score [52]. However, adding the genetic risk score results to models incorporating traditional cardiovascular risk factors did not have a significant impact on discrimination or reclassification. It may be that known clinical risk factors are on the causal pathway to cardiovascular disease and therefore the genetic risk scores add little in terms of predictive capability in those with established risk factors, such as most patients with diabetes.
Risk Prediction in the Era of Big Data and Machine-Learning
Increasingly large datasets are being utilised for the development of risk prediction tools due to advancing technology in a number of fields, such as electronic health records, genomics, proteomics and metabolomics. Such data have posed challenges to traditional regression-based methods for creating predictive models. Analytic challenges include assumptions of linear relationships between risk factors and outcomes, interactions between covariates and large numbers of predictor variables. Machine learning methods have been developed to aid in harnessing the potential predictive capabilities of so-called big data. Machine learning algorithms perform automated stochastic (random) or deterministic searches for the predictive model with optimal fit within certain specified constraints [53]. Typically, the resulting models are less useful for understanding associations or causal relationships than traditional regression analyses, but can have stronger predictive capabilities.
Electronic health records have been rapidly adopted in recent years and potentially offer a wealth of data for use in risk prediction research. However, much of this information is stored as unstructured data. This has led to the development of information extraction and data mining systems which search electronic records and identify relevant cardiovascular risk factor data [54]. These systems utilise a combination of machine learning and rule based clinical text mining techniques.
Cardiovascular risk prediction models utilising machine learning are yet to be adopted into routine clinical practice and international guidelines. Nevertheless, there is ever increasing research using non-traditional data sources and potential novel prognostic biomarkers which is likely to impact clinical risk prediction and treatment decision making in the coming years.
Conclusions
The prevalence of diabetes, particularly type 2 diabetes, continues to grow worldwide, contributing greatly to the global burden of cardiovascular disease. Despite substantial improvements in rates of cardiovascular events and mortality in recent decades, the absolute number of premature deaths due to cardiovascular disease continues to rise in low- and middle-income countries in the setting of population growth and ageing. People with diabetes have a significantly increased risk of cardiovascular disease, but the gap compared to those without diabetes has narrowed in some populations, likely as a result of improved management of multiple risk factors and better systems of care. The management of cardiovascular risk remains a cornerstone of diabetes care. While ischaemic heart disease remains the most common cause of mortality in diabetes, heart failure and peripheral arterial disease are now the most common presenting cardiovascular complications.
Strong evidence from clinical trials supports aggressive risk factor modification in patients with established cardiovascular disease, so-called secondary prevention. Multifactorial therapy to modify cardiovascular risk in diabetes has also been shown to be effective as primary