Miller, Rachel G
(2016)
Cardiovascular disease in Type 1 diabetes: quantifying risk and addressing limitations in the analysis of longitudinal cohort studies.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Cardiovascular disease (CVD) has historically been increased in type 1 diabetes compared to the
general population, but no contemporary estimates of risk are available in the United States.
Additionally, the reasons for this increased risk are not fully understood, as the hyperglycemia
that characterizes type 1 diabetes is itself an inconsistent predictor of CVD incidence. Thus, the
objective of this dissertation is to quantify the contemporary incidence and excess risk of CVD in
young adults <45 years old with type 1 diabetes and to utilize novel statistical methods to
address limitations in the analyses of longitudinal cohort studies, in an effort to better understand
the risk factor patterns that lead to CVD in this population.
Data are from the Pittsburgh Epidemiology of Diabetes Complications study, a
prospective cohort study of childhood-onset type 1 diabetes diagnosed at Children’s Hospital of
Pittsburgh between 1950 and 1980. CVD data from the background Allegheny County,
Pennsylvania population were used to calculate age- and sex-matched standardized mortality
(SMR) and incidence rate ratios (IRR). Using tree-structured survival analysis (TSSA), formal
subgroup analysis was performed to identify groups at varying levels of risk for CVD, based on
threshold effects of continuous risk factors. Joint models were used to simultaneously model the
longitudinal trajectory of HbA1c and time to CVD incidence.
CVD risk was shown remain significantly increased in this type 1 diabetes cohort. TSSA
identified a range of risk groups, which were defined by combinations of diabetes duration, non-
HDL cholesterol, albumin excretion rate, and white blood cell count. The longitudinal trajectory
of HbA1c was associated with CVD risk, similarly across all manifestations of CVD, including
coronary artery disease, stroke, and lower extremity arterial disease, which is a new finding in
this cohort. This work has important impacts on public health, as it confirms that individuals
with type 1 diabetes continue to be at increased risk for CVD and demonstrates that novel
statistical methods should be utilized as a complement to traditional methods to increase
understanding of disease etiology.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
12 September 2016 |
| Date Type: |
Publication |
| Defense Date: |
20 July 2016 |
| Approval Date: |
12 September 2016 |
| Submission Date: |
14 July 2016 |
| Access Restriction: |
3 year -- Restrict access to University of Pittsburgh for a period of 3 years. |
| Number of Pages: |
151 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Epidemiology |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Type 1 Diabetes, Cardiovascular Disease, Risk Stratification, Tree-Structured Survival Analysis, Joint Models, Longitudinal Data, Cohort Studies, Glycemic Control |
| Date Deposited: |
12 Sep 2016 16:02 |
| Last Modified: |
01 Sep 2019 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/28541 |
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