ObjectiveDetection of subclinical cardiovascular disease (CVD) has significant impact on the management of type 2 diabetes. We examined whether the assessment of diabetic retinopathy (DR) is useful for identifying patients at a higher risk of having silent CVD.Research design and methodsProspective case–control study comprising 200 type 2 diabetic subjects without history of clinical CVD and 60 age-matched non-diabetic subjects. The presence of subclinical CVD was examined using two parameters: (1) calcium coronary score (CACs); (2) composite of CACs >400 UA, carotid plaque ≥3 mm, carotid intima–media thickness ratio >1, or the presence of ECG changes suggestive of previous asymptomatic myocardial infarction. In addition, coronary angio-CT was performed. DR was assessed by slit-lamp biomicroscopy and retinography.ResultsType 2 diabetic subjects presented higher CACs than non-diabetic control subjects (p<0.01). Age, male gender, and the presence of DR were independently related to CACs >400 (area under the receiver operating characteristic curve (AUROC) 0.76). In addition, an inverse relationship was observed between the degree of DR and CACs <10 AU. The variables independently associated with the composite measurement of subclinical CVD were age, diabetes duration, the glomerular filtration rate, microalbuminuria, and the presence of DR (AUROC 0.71). In addition, a relationship (p<0.01) was observed between the presence and degree of DR and coronary stenosis.ConclusionsThe presence and degree of DR is independently associated with subclinical CVD in type 2 diabetic patients. Our results lead us to propose a rationalized screening for coronary artery disease in type 2 diabetes based on prioritizing patients with DR, particularly those with moderate–severe degree.
Cardiovascular diseases (CVD) are one of the leading causes of death in the developed countries. Previous studies suggest that retina blood vessels provide relevant information on cardiovascular risk. Retina fundus imaging (RFI) is a cheap medical imaging test that is already regularly performed in diabetic population as screening of diabetic retinopathy (DR). Since diabetes is a major cause of CVD, we wanted to explore the use Deep Learning architectures on RFI as a tool for predicting CV risk in this population. Particularly, we use the coronary artery calcium (CAC) score as a marker, and train a convolutional neural network (CNN) to predict whether it surpasses a certain threshold defined by experts. The preliminary experiments on a reduced set of clinically verified patients show promising accuracies. In addition, we observed that elementary clinical data is positively correlated with the risk of suffering from a CV disease. We found that the results from both informational cues are complementary, and we propose two applications that can benefit from the combination of image analysis and clinical data.
The incidence and prevalence of diabetes are increasing worldwide, and cardiovascular disease (CVD) is the leading cause of death among subjects with type 2 diabetes (T2D). The assessment and stratification of cardiovascular risk in subjects with T2D is a challenge. Advanced glycation end products are heterogeneous molecules produced by non-enzymatic glycation of proteins, lipids, or nucleic acids. Accumulation of advanced glycation end products is increased in subjects with T2D and is considered to be one of the major pathogenic mechanism in developing complications in diabetes. Skin AGEs could be assessed by skin autofluorescence. This method has been validated and related to the presence of micro and macroangiopathy in individuals with type 2 diabetes. In this context, the aim of this review is to critically summarize current knowledge and scientific evidence on the relationship between skin AGEs and CVD in subjects with type 2 diabetes, with a brief reference to other diabetes-related complications.
Risk of cardiovascular events is not homogeneous in subjects with type 2 diabetes; therefore, its early identification remains a challenge to be met. The aim of this study is to evaluate whether the presence of diabetic retinopathy and accumulation of advanced glycation end-products in subcutaneous tissue can help identify patients at high risk of cardiovascular events. For this purpose, we conducted a prospective study (mean follow-up: 4.35 years) comprising 200 subjects with type 2 diabetes with no history of clinical cardiovascular disease and 60 non-diabetic controls matched by age and sex. The primary outcome was defined as the composite of myocardial infarction, coronary revascularization, stroke, lower limb amputation or cardiovascular death. The Cox proportional hazard multiple regression analysis was used to determine the independent predictors of cardiovascular events. The patients with type 2 diabetes had significantly more cardiovascular events than the non-diabetic subjects. Apart from the classic factors such as age, sex and coronary artery calcium score, we observed that the diabetic retinopathy and advanced glycation end-products in subcutaneous tissue were independent predictors of cardiovascular events. We conclude that the diabetic retinopathy and advanced glycation end-products in subcutaneous tissue could be useful biomarkers for selecting type 2 diabetic patients in whom the screening for cardiovascular disease should be prioritized, thereby creating more personalized and cost-effective medicine.
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