Diabetic retinopathy (DR), a common microvascular complication, has consistently been shown to be associated with an increased risk of cardiovascular disease (CVD). This book provides complete coverage of DR as a potential marker for CVD in those with diabetes. It succinctly reviews the epidemiological and pathogenic links of DR to various cardiovascular events including stroke, coronary artery disease, chronic kidney disease, heart failure, and mortality. Furthermore, it discusses the usefulness of DR in CVD risk prediction and cardiovascular safety of anti-VEGF therapy in diabetic patients. There are insights from contemporary diabetic trials that demonstrated the enhanced cardiovascular benefit of novel glucose lowering therapy. It also highlights the potential of novel retinal imaging to predict CVD and its risk factors using the state-of-the art artificial intelligence-based deep learning systems. This book will be an invaluable resource for specialists translating research findings into clinical care, including those in cardiology, endocrinology, ophthalmology, and general practitioners. IT will also be of interest to public health practitioners, researchers, graduate students, and biotech companies interested in developing retinal image-based diagnostic and prognostic tools.
2019. "Preface", Diabetic Retinopathy and Cardiovascular Disease, Charumathi Sabanayagam, Tien Y. Wong, Tien Y. Wong, Charumathi Sabanayagam, Massimo Porta
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Cardiovascular disease (CVD) is a major cause of morbidity and mortality in patients with diabetes. Diabetic retinopathy (DR), which is a common microvascular complication of diabetes, is a leading cause of blindness in working-age adults. Beyond the eye, retinopathy changes in diabetic patients have been shown to carry an increased risk for cardiovascular events including coronary heart disease, heart failure, stroke, all-cause and CVD mortality. In addition to clinical CVD, DR has also been shown to be associated with subclinical measures of atherosclerosis. Independent of diabetes, chronic kidney disease (CKD) is a major risk factor for CVD and death due to CVD accounts for up to 50% of all deaths in patients with moderate and severe CKD. DR is a well-known risk factor for CKD. DR shares many risk factors, genetic and pathogenic pathways with heart, brain and kidney and a simple non-invasive assessment of DR could improve risk stratification of CVD, in particular in those with diabetes. In the last 5 years, advances in artificial-intelligence based deep learning and computer vision have opened new possibilities of using retinal images to predict DR with accuracy comparable to that of human experts and other systemic diseases including stroke, and dementia. Poplin et al. in a recent study demonstrated the application of retinal image-based deep learning algorithms to predict cardiovascular risk factors. While the impact of DR on vision is well known, its many associations with clinical and subclinical CVD are less recognized. In this book, we comprehensively reviewed the epidemiological evidence and pathogenic links of DR to various cardiovascular events and implications for assessment of retina in cardiovascular risk prediction. The book comprises 10 chapters:
1 Cardiovascular disease risk in diabetes mellitus
2 Epidemiology and risk factors for diabetic retinopathy
3 Diabetic retinopathy and stroke
4 Diabetic retinopathy and heart disease
5 Diabetic retinopathy and chronic kidney disease
6 Diabetic retinopathy and mortality
7 Retinal vascular changes in diabetes and dementia
8 Anti-vascular endothelial growth factor therapy and CVD risk
9 Novel retinal imaging in assessment of cardiovascular risk factors and diseases
10 Reducing cardiovascular risk in diabetes: Insights from diabetes trials
Starting with overview of CVD risk in diabetes, the book covers in detail the links of DR with each of the CVD outcome including stroke, coronary artery disease, heart failure, CKD, dementia and mortality and the usefulness of DR in CVD risk prediction. Antiangiogenic therapy using anti-vascular endothelial growth factor (anti-VEGF) is the standard care for the treatment of diabetic macular oedema. However, concerns about their safety have been raised, since diabetic patients are at increased risk for cardiovascular events and systemic adverse events such as arterial thromboembolic events due to VEGF inhibition. To address this concern, a separate chapter is dedicated to reviewing the current evidence from clinical trials on cardiovascular safety of anti-VEGF therapy in diabetic patients treated for diabetic macular oedema. The book also highlights the potential of novel retinal imaging to predict CVD and its risk factors using the state-of-the art artificial intelligence-based deep learning systems. The last chapter provides insights from contemporary diabetic trials that demonstrated the enhanced cardiovascular benefit of novel glucose lowering therapy.
Authors who contributed to book chapters are expert researchers in their individual fields, many of whom are leading international authorities. We hope this book will be an invaluable resource for specialists translating research findings into clinical care including those in cardiology, endocrinology, ophthalmology, general practitioners, and public health practitioners, researchers, graduate students, as well as biotech companies interested in developing retinal image-based diagnostic and prognostic tools.
Charumathi Sabanayagam, Singapore
Tien Y. Wong, Singapore
