Abstract
Background Optimal control of traditional risk factors only partially attenuates the exceeding cardiovascular mortality of individuals with diabetes. Employment of machine learning (ML) techniques aimed at the identification of novel features of risk prediction is a compelling target to tackle residual cardiovascular risk. The objective of this study is to identify clinical phenotypes of T2D which are more prone to developing cardiovascular disease. Methods The Brazilian Diabetes Study is a single-center, ongoing, prospective registry of T2D individuals. Eligible patients are 30 years old or older, with a confirmed T2D diagnosis. After an initial visit for the signature of the informed consent form and medical history registration, all volunteers undergo biochemical analysis, echocardiography, carotid ultrasound, ophthalmologist visit, dual x-ray absorptiometry, coronary artery calcium score, polyneuropathy assessment, advanced glycation end-products reader, and ambulatory blood pressure monitoring. A 5-year follow-up will be conducted by yearly phone interviews for endpoints disclosure. The primary endpoint is the difference between ML-based clinical phenotypes in the incidence of a composite of death, myocardial infarction, revascularization, and stroke. Since June/2016, 1030 patients (mean age: 57 years, diabetes duration of 9.7 years, 58% male) were enrolled in our study. The mean follow-up time was 3.7 years in October/2021. Conclusion The BDS will be the first large population-based cohort dedicated to the identification of clinical phenotypes of T2D at higher risk of cardiovascular events. Data derived from this study will provide valuable information on risk estimation and prevention of cardiovascular and other diabetes-related events. ClinicalTrials.gov Identifier NCT04949152
Highlights
The steady increase in the global prevalence of diabetes, currently estimated to be as high as 463 million individuals, has fueled the burden of cardiovascular disease and other diabetes-related complications.[1]
Employment of machine learning (ML) techniques aimed at identification of novel features of risk prediction is a compelling target to tackle residual cardiovascular risk
The Brazilian Diabetes Study (BDS) will be the first large population-based cohort dedicated to the identification of clinical phenotypes of T2D at higher risk of cardiovascular events
Summary
The steady increase in the global prevalence of diabetes, currently estimated to be as high as 463 million individuals, has fueled the burden of cardiovascular disease and other diabetes-related complications.[1]. Prevention of diabetic complications has been grounded by the achievement of stricter control of traditional risk factors, such as glycated hemoglobin, low-density-lipoprotein cholesterol, and blood pressure.[5,6] Though reasonable, this strategy does not fully address the complex, multifactorial pathophysiology of diabetes.[7] As a matter of fact, cardiovascular mortality remains augmented even among individuals with optimal metabolic control.[8] Thereafter, growing attention has been directed to the development of risk prediction models dedicated to early detection of individuals at higher risk of complications to whom earlier tailored clinical interventions yield the greater therapeutical benefit.[5]. Optimal control of traditional risk factors only partially attenuates the exceeding cardiovascular mortality of individuals with diabetes. Employment of machine learning (ML) techniques aimed at identification of novel features of risk prediction is a compelling target to tackle residual cardiovascular risk
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