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

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 cardiov... Full description

Journal Title: Current medical research and opinion 2022, Vol.38 (4), p.523-529
Main Author: Barreto, Joaquim
Other Authors: Wolf, Vaneza , Bonilha, Isabella , Luchiari, Beatriz , Lima, Marcus , Oliveira, Alessandra , Vitte, Sofia , Machado, Gabriela , Cunha, Jessica , Borges, Cynthia , Munhoz, Daniel , Fernandes, Vicente , Kimura-Medorima, Sheila Tatsumi , Breder, Ikaro , Fernandez, Marta Duran , Quinaglia, Thiago , Oliveira, Rodrigo B. , Chaves, Fernando , Arieta, Carlos , Guerra-Júnior, Gil , Avila, Sandra , Nadruz, Wilson , Carvalho, Luiz Sergio F. , Sposito, Andrei C.
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: England: Taylor & Francis
ID: ISSN: 0300-7995
Link: https://www.ncbi.nlm.nih.gov/pubmed/35174749
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title: Rationale and design of the Brazilian diabetes study: a prospective cohort of type 2 diabetes
format: Article
creator:
  • Barreto, Joaquim
  • Wolf, Vaneza
  • Bonilha, Isabella
  • Luchiari, Beatriz
  • Lima, Marcus
  • Oliveira, Alessandra
  • Vitte, Sofia
  • Machado, Gabriela
  • Cunha, Jessica
  • Borges, Cynthia
  • Munhoz, Daniel
  • Fernandes, Vicente
  • Kimura-Medorima, Sheila Tatsumi
  • Breder, Ikaro
  • Fernandez, Marta Duran
  • Quinaglia, Thiago
  • Oliveira, Rodrigo B.
  • Chaves, Fernando
  • Arieta, Carlos
  • Guerra-Júnior, Gil
  • Avila, Sandra
  • Nadruz, Wilson
  • Carvalho, Luiz Sergio F.
  • Sposito, Andrei C.
subjects:
  • Blood Pressure Monitoring, Ambulatory
  • Brazil - epidemiology
  • cardiovascular disease
  • Cohort Studies
  • Diabetes
  • Diabetes Mellitus, Type 2 - diagnosis
  • Female
  • Humans
  • machine learning
  • Male
  • Myocardial Infarction
  • Risk Factors
  • risk prediction
ispartof: Current medical research and opinion, 2022, Vol.38 (4), p.523-529
description: 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. 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. 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. NCT04949152
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 0300-7995
fulltext: fulltext
issn:
  • 0300-7995
  • 1473-4877
url: Link


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titleRationale and design of the Brazilian diabetes study: a prospective cohort of type 2 diabetes
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creatorcontribBarreto, Joaquim ; Wolf, Vaneza ; Bonilha, Isabella ; Luchiari, Beatriz ; Lima, Marcus ; Oliveira, Alessandra ; Vitte, Sofia ; Machado, Gabriela ; Cunha, Jessica ; Borges, Cynthia ; Munhoz, Daniel ; Fernandes, Vicente ; Kimura-Medorima, Sheila Tatsumi ; Breder, Ikaro ; Fernandez, Marta Duran ; Quinaglia, Thiago ; Oliveira, Rodrigo B. ; Chaves, Fernando ; Arieta, Carlos ; Guerra-Júnior, Gil ; Avila, Sandra ; Nadruz, Wilson ; Carvalho, Luiz Sergio F. ; Sposito, Andrei C. ; Brazilian Heart Study Group
descriptionOptimal 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. 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. 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. NCT04949152
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descriptionOptimal 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. 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. 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. NCT04949152
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titleRationale and design of the Brazilian diabetes study: a prospective cohort of type 2 diabetes
authorBarreto, Joaquim ; Wolf, Vaneza ; Bonilha, Isabella ; Luchiari, Beatriz ; Lima, Marcus ; Oliveira, Alessandra ; Vitte, Sofia ; Machado, Gabriela ; Cunha, Jessica ; Borges, Cynthia ; Munhoz, Daniel ; Fernandes, Vicente ; Kimura-Medorima, Sheila Tatsumi ; Breder, Ikaro ; Fernandez, Marta Duran ; Quinaglia, Thiago ; Oliveira, Rodrigo B. ; Chaves, Fernando ; Arieta, Carlos ; Guerra-Júnior, Gil ; Avila, Sandra ; Nadruz, Wilson ; Carvalho, Luiz Sergio F. ; Sposito, Andrei C.
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abstractOptimal 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. 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. 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. NCT04949152
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doi10.1080/03007995.2022.2043658
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