Hemoglobin A1c Level and Cardiovascular Disease Incidence in Persons With Type 1 Diabetes: An Application of Joint Modeling of Longitudinal and Time-to-Event Data in the Pittsburgh Epidemiology of Diabetes Complications Study

Abstract Type 1 diabetes (T1D) is associated with increased risk of cardiovascular disease (CVD), but hyperglycemia (measured by hemoglobin A1c (HbA1c) level), which characterizes T1D, has itself been an inconsistent predictor of CVD incidence. However, only baseline HbA1c or a summary measure (e.g....

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Published inAmerican journal of epidemiology Vol. 187; no. 7; pp. 1520 - 1529
Main Authors Miller, Rachel G, Anderson, Stewart J, Costacou, Tina, Sekikawa, Akira, Orchard, Trevor J
Format Journal Article
LanguageEnglish
Published United States Oxford University Press 01.07.2018
Oxford Publishing Limited (England)
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Online AccessGet full text
ISSN0002-9262
1476-6256
1476-6256
DOI10.1093/aje/kwx386

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Abstract Abstract Type 1 diabetes (T1D) is associated with increased risk of cardiovascular disease (CVD), but hyperglycemia (measured by hemoglobin A1c (HbA1c) level), which characterizes T1D, has itself been an inconsistent predictor of CVD incidence. However, only baseline HbA1c or a summary measure (e.g., mean level over follow-up) is usually analyzed. Joint models allow simultaneous modeling of repeatedly measured longitudinal covariates, using random effects, and time-to-event data. We evaluated data from the Pittsburgh Epidemiology of Diabetes Complications Study, an ongoing prospective cohort study of childhood-onset T1D that has followed participants since 1986–1988 and has repeatedly found little association between baseline HbA1c or mean follow-up HbA1c and coronary artery disease incidence. Of 561 participants without CVD at baseline, 263 (46.9%) developed CVD over a period of 25 years (1986–2014). In joint models, each 1% unit increase in HbA1c trajectory was associated with a 1.26-fold increased risk of CVD (95% confidence interval: 1.07, 1.45), after adjustment for baseline levels of other CVD risk factors, and a 1.13-fold increased risk (95% confidence interval: 0.99, 1.32) after adjustment for updated mean levels of other CVD risk factors. These findings, which support the need for good glycemic control to prevent CVD in persons with T1D, underscore the importance of utilizing methods incorporating within-subject variation over time when analyzing and interpreting longitudinal cohort study data.
AbstractList Abstract Type 1 diabetes (T1D) is associated with increased risk of cardiovascular disease (CVD), but hyperglycemia (measured by hemoglobin A1c (HbA1c) level), which characterizes T1D, has itself been an inconsistent predictor of CVD incidence. However, only baseline HbA1c or a summary measure (e.g., mean level over follow-up) is usually analyzed. Joint models allow simultaneous modeling of repeatedly measured longitudinal covariates, using random effects, and time-to-event data. We evaluated data from the Pittsburgh Epidemiology of Diabetes Complications Study, an ongoing prospective cohort study of childhood-onset T1D that has followed participants since 1986–1988 and has repeatedly found little association between baseline HbA1c or mean follow-up HbA1c and coronary artery disease incidence. Of 561 participants without CVD at baseline, 263 (46.9%) developed CVD over a period of 25 years (1986–2014). In joint models, each 1% unit increase in HbA1c trajectory was associated with a 1.26-fold increased risk of CVD (95% confidence interval: 1.07, 1.45), after adjustment for baseline levels of other CVD risk factors, and a 1.13-fold increased risk (95% confidence interval: 0.99, 1.32) after adjustment for updated mean levels of other CVD risk factors. These findings, which support the need for good glycemic control to prevent CVD in persons with T1D, underscore the importance of utilizing methods incorporating within-subject variation over time when analyzing and interpreting longitudinal cohort study data.
Type 1 diabetes (T1D) is associated with increased risk of cardiovascular disease (CVD), but hyperglycemia (measured by hemoglobin A 1c (HbA1c) level), which characterizes T1D, has itself been an inconsistent predictor of CVD incidence. However, only baseline HbA1c or a summary measure (e.g., mean level over follow-up) is usually analyzed. Joint models allow simultaneous modeling of repeatedly measured longitudinal covariates, using random effects, and time-to-event data. We evaluated data from the Pittsburgh Epidemiology of Diabetes Complications Study, an ongoing prospective cohort study of childhood-onset T1D that has followed participants since 1986–1988 and has repeatedly found little association between baseline HbA1c or mean follow-up HbA1c and coronary artery disease incidence. Of 561 participants without CVD at baseline, 263 (46.9%) developed CVD over a period of 25 years (1986–2014). In joint models, each 1% unit increase in HbA1c trajectory was associated with a 1.26-fold increased risk of CVD (95% confidence interval: 1.07, 1.45), after adjustment for baseline levels of other CVD risk factors, and a 1.13-fold increased risk (95% confidence interval: 0.99, 1.32) after adjustment for updated mean levels of other CVD risk factors. These findings, which support the need for good glycemic control to prevent CVD in persons with T1D, underscore the importance of utilizing methods incorporating within-subject variation over time when analyzing and interpreting longitudinal cohort study data.
Type 1 diabetes (T1D) is associated with increased risk of cardiovascular disease (CVD), but hyperglycemia (measured by hemoglobin A1c (HbA1c) level), which characterizes T1D, has itself been an inconsistent predictor of CVD incidence. However, only baseline HbA1c or a summary measure (e.g., mean level over follow-up) is usually analyzed. Joint models allow simultaneous modeling of repeatedly measured longitudinal covariates, using random effects, and time-to-event data. We evaluated data from the Pittsburgh Epidemiology of Diabetes Complications Study, an ongoing prospective cohort study of childhood-onset T1D that has followed participants since 1986–1988 and has repeatedly found little association between baseline HbA1c or mean follow-up HbA1c and coronary artery disease incidence. Of 561 participants without CVD at baseline, 263 (46.9%) developed CVD over a period of 25 years (1986–2014). In joint models, each 1% unit increase in HbA1c trajectory was associated with a 1.26-fold increased risk of CVD (95% confidence interval: 1.07, 1.45), after adjustment for baseline levels of other CVD risk factors, and a 1.13-fold increased risk (95% confidence interval: 0.99, 1.32) after adjustment for updated mean levels of other CVD risk factors. These findings, which support the need for good glycemic control to prevent CVD in persons with T1D, underscore the importance of utilizing methods incorporating within-subject variation over time when analyzing and interpreting longitudinal cohort study data.
Type 1 diabetes (T1D) is associated with increased risk of cardiovascular disease (CVD), but hyperglycemia (measured by hemoglobin A1c (HbA1c) level), which characterizes T1D, has itself been an inconsistent predictor of CVD incidence. However, only baseline HbA1c or a summary measure (e.g., mean level over follow-up) is usually analyzed. Joint models allow simultaneous modeling of repeatedly measured longitudinal covariates, using random effects, and time-to-event data. We evaluated data from the Pittsburgh Epidemiology of Diabetes Complications Study, an ongoing prospective cohort study of childhood-onset T1D that has followed participants since 1986-1988 and has repeatedly found little association between baseline HbA1c or mean follow-up HbA1c and coronary artery disease incidence. Of 561 participants without CVD at baseline, 263 (46.9%) developed CVD over a period of 25 years (1986-2014). In joint models, each 1% unit increase in HbA1c trajectory was associated with a 1.26-fold increased risk of CVD (95% confidence interval: 1.07, 1.45), after adjustment for baseline levels of other CVD risk factors, and a 1.13-fold increased risk (95% confidence interval: 0.99, 1.32) after adjustment for updated mean levels of other CVD risk factors. These findings, which support the need for good glycemic control to prevent CVD in persons with T1D, underscore the importance of utilizing methods incorporating within-subject variation over time when analyzing and interpreting longitudinal cohort study data.Type 1 diabetes (T1D) is associated with increased risk of cardiovascular disease (CVD), but hyperglycemia (measured by hemoglobin A1c (HbA1c) level), which characterizes T1D, has itself been an inconsistent predictor of CVD incidence. However, only baseline HbA1c or a summary measure (e.g., mean level over follow-up) is usually analyzed. Joint models allow simultaneous modeling of repeatedly measured longitudinal covariates, using random effects, and time-to-event data. We evaluated data from the Pittsburgh Epidemiology of Diabetes Complications Study, an ongoing prospective cohort study of childhood-onset T1D that has followed participants since 1986-1988 and has repeatedly found little association between baseline HbA1c or mean follow-up HbA1c and coronary artery disease incidence. Of 561 participants without CVD at baseline, 263 (46.9%) developed CVD over a period of 25 years (1986-2014). In joint models, each 1% unit increase in HbA1c trajectory was associated with a 1.26-fold increased risk of CVD (95% confidence interval: 1.07, 1.45), after adjustment for baseline levels of other CVD risk factors, and a 1.13-fold increased risk (95% confidence interval: 0.99, 1.32) after adjustment for updated mean levels of other CVD risk factors. These findings, which support the need for good glycemic control to prevent CVD in persons with T1D, underscore the importance of utilizing methods incorporating within-subject variation over time when analyzing and interpreting longitudinal cohort study data.
Author Costacou, Tina
Miller, Rachel G
Anderson, Stewart J
Orchard, Trevor J
Sekikawa, Akira
AuthorAffiliation 2 Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
1 Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
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Copyright The Author(s) 2018. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. 2018
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Keywords random effects
cardiovascular disease
type 1 diabetes
glycemic control
longitudinal studies
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Snippet Abstract Type 1 diabetes (T1D) is associated with increased risk of cardiovascular disease (CVD), but hyperglycemia (measured by hemoglobin A1c (HbA1c) level),...
Type 1 diabetes (T1D) is associated with increased risk of cardiovascular disease (CVD), but hyperglycemia (measured by hemoglobin A1c (HbA1c) level), which...
Type 1 diabetes (T1D) is associated with increased risk of cardiovascular disease (CVD), but hyperglycemia (measured by hemoglobin A 1c (HbA1c) level), which...
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StartPage 1520
SubjectTerms Adult
Cardiovascular disease
Cardiovascular diseases
Cardiovascular Diseases - epidemiology
Cardiovascular Diseases - etiology
Children
Confidence intervals
Coronary artery
Coronary artery disease
Diabetes
Diabetes mellitus
Diabetes mellitus (insulin dependent)
Diabetes Mellitus, Type 1 - blood
Diabetes Mellitus, Type 1 - complications
Epidemiology
Female
Glycated Hemoglobin A - analysis
Health risks
Heart diseases
Hemoglobin
Humans
Hyperglycemia
Incidence
Longitudinal Studies
Male
Modelling
Models, Statistical
Pennsylvania - epidemiology
Practice of Epidemiology
Prospective Studies
Risk analysis
Risk Factors
Time Factors
Title Hemoglobin A1c Level and Cardiovascular Disease Incidence in Persons With Type 1 Diabetes: An Application of Joint Modeling of Longitudinal and Time-to-Event Data in the Pittsburgh Epidemiology of Diabetes Complications Study
URI https://www.ncbi.nlm.nih.gov/pubmed/29394305
https://www.proquest.com/docview/2306242036
https://www.proquest.com/docview/1993990712
https://pubmed.ncbi.nlm.nih.gov/PMC6030923
Volume 187
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