Rationale and design of the Brazilian diabetes study: a prospective cohort of type 2 diabetes

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

Keywords:

• Diabetes
• cardiovascular disease
• risk prediction
• machine learning

Transparency

Declaration of funding

Andrei C Sposito is supported by a Research Career Awards grant from the Brazilian National Research Council (CNPq) (grant number 301465/2017-7). Sandra Avila is partially funded by CNPq PQ-2 315231/2020-3, FAPESP 2013/08293-7, and Google LARA 2020.

Declaration of financial/other relationships

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

Professor Carvalho had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: JB, BL, ACS. Acquisition, analysis, or interpretation of data: JB, VW, IB, BL, ML, AL, SV, GM, JC, CB, DM, VF, STK, IB, MDF, TQ, RBO, FC, CA, GGJ, SA, WN, LSFC. Drafting of the manuscript: JB, BL, ACS. Critical revision of the manuscript for important intellectual content: All authors. The manuscript has been reviewed by all authors, who agree with the analysis of the data and the conclusions reached.

Data availability statement

The Brazilian Diabetes Study investigators will hold intellectual property over data. Its availability may be considered upon reasonable request.

Ethics statement

The study was approved by the local ethics committee (CAAE: 89525518.8.1001.5404) and complied with the Declaration of Helsinki principles.