- Title Information
- Title
- From Metabolites to Myocardial Infarction: Translational Bioinformatics of Atherosclerotic Cardiovascular Disease
- Type of Resource (primo)
- dissertations
- Name:
Personal
- Name Part
- Eisman, Aaron Seth
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Sarkar, Indra
- Role
- Role Term:
Text
- Advisor
- Name:
Personal
- Name Part
- Chen, Elizabeth
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Istrail, Sorin
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Wu, Wen-Chih
- Role
- Role Term:
Text
- Reader
- Name:
Corporate
- Name Part
- Brown University. Center for Computational Molecular Biology
- Role
- Role Term:
Text
- sponsor
- Origin Information
- Copyright Date
- 2023
- Physical Description
- Extent
- 17, 191 p.
- digitalOrigin
- born digital
- Note:
thesis
- Thesis (Ph. D.)--Brown University, 2023
- Genre (aat)
- theses
- Abstract
- Risk factor-based prediction is the cornerstone of guidelines for the primary prevention of atherosclerotic cardiovascular disease (ASCVD). Despite clear criteria for statin therapy, ASCVD remains the leading cause of death and disability in the United States. Many "high-risk" individuals go untreated, and others considered "low-risk" develop myocardial infarction or stroke without warning. This dissertation addresses foundational requirements for improving the primary prevention of ASCVD through the introduction of a translational bioinformatics framework that integrates (1) health informatics solutions to characterize population ASCVD risk and prevention, (2) bioinformatics methods to probe the underlying biology of atherosclerosis, and (3) genomic-linked electronic health records to propose novel biomarkers for incident ASCVD. Population ASCVD risk, primary prevention practices, and incident disease were evaluated using data aggregated from more than 50 healthcare institutions by a statewide health information exchange standardized to a common data model. This demonstrated fragmentation in ASCVD risk factor data collection across healthcare sites for individual patients and a significant opportunity to improve population statin adherence. Advancements in ASCVD prediction and patient risk stratification will be informed by causal biomarkers of atherosclerosis. Towards this goal, whole genomic, aptamer-based proteomic, and mass spectrometry-based metabolomic data derived from banked samples were studied to identify novel proteomic determinants of metabolite levels in human plasma. Mendelian randomization identified previously uncharacterized causal relationships that involve known ASCVD biomarkers. The top findings from this study were validated by performing serum metabolomics on murine knockout models. The gene-protein-metabolite associations from the bioinformatics study were integrated with whole genome sequencing linked electronic health records from the All of Us Research Program. This analysis rediscovered the relationship between apolipoprotein E and the development of ASCVD and proposed a role for both the surface protein CD36 and plasmalogen glycerophospholipids in the causal pathway with incident myocardial infarction. Together, these studies lay the groundwork for future investigation of the biological basis of atherosclerosis and improved primary prevention of cardiovascular disease.
- Subject
- Topic
- Statins
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00832181")
- Topic
- Bioinformatics
- Subject
- Topic
- Atherosclerotic cardiovascular disease
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00849829")
- Topic
- Causation
- Subject
- Topic
- biomedical informatics
- Subject
- Topic
- translational bioinformatics
- Subject
- Topic
- medical guidelines
- Language
- Language Term (ISO639-2B)
- English
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20230602