Multi-omics studies of aging and neurodegenerative disease in human cohorts
Abstract/Contents
- Abstract
- Aging is the number one risk factor for everything from heart disease to cancer to broken bones to neurodegeneration. The molecular details of how aging drives such diverse disease processes are still largely unknown. A critical limitation to such study in humans is the lack of reliable molecular biomarkers of aging in different organs. This is a particularly severe problem for the brain and the study of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease, the two most common forms of neurodegeneration, where damage is thought to accumulate silently for many years before symptoms emerge. This dissertation is focused on the question of biomarkers for aging generally and on early biomarkers of neurodegenerative disease in particular. Here, I take a data-driven approach to the discovery of new biomarkers using biofluid proteomics, the quantitative analysis of thousands of proteins in plasma, cerebral spinal fluid, and urine. In Chapter 1, I give a general overview of the fields of biomarkers for aging and neurodegeneration, with a focus on data-driven and machine-learning approaches. Chapter 2 presents a machine-learning approach to the development of organ-specific biomarkers of aging using plasma proteomics from thousands of people across the adult lifespan. We create biomarkers for 11 different organs which can be used to predict future health and disease outcomes, and we do a deep dive into how the aging of different organs contributes to Alzheimer's disease onset, progression, and general cognitive decline. Chapter 3 presents the discovery and characterization of a new molecular biomarker of Parkinson's disease using cerebrospinal fluid, plasma, and urine proteomics. This biomarker, AADC, can predict disease onset before symptoms appear and can predict future disease progression, two urgently unmet needs in the field of Parkinson's disease research and clinical care. Together, these studies demonstrate the power of data-driven proteomics for biomarker research in aging and neurodegeneration, and provide analytical frameworks and large resources of human data for future study.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Rutledge, Jarod Evert |
|---|---|
| Degree supervisor | Montgomery, Stephen, 1979- |
| Degree supervisor | Wyss-Coray, Anton |
| Thesis advisor | Montgomery, Stephen, 1979- |
| Thesis advisor | Wyss-Coray, Anton |
| Thesis advisor | Jerby, Livnat |
| Thesis advisor | Snyder, Michael, Ph. D. |
| Degree committee member | Jerby, Livnat |
| Degree committee member | Snyder, Michael, Ph. D. |
| Associated with | Stanford University, School of Medicine |
| Associated with | Stanford University, Department of Genetics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Jarod Rutledge. |
|---|---|
| Note | Submitted to the Department of Genetics. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/hd238wn6584 |
Access conditions
- Copyright
- © 2023 by Jarod Evert Rutledge
- License
- This work is licensed under a Creative Commons Attribution Non Commercial No Derivatives 3.0 Unported license (CC BY-NC-ND).
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