Electronic Thesis and Dissertation Repository

Thesis Format

Monograph

Degree

Master of Engineering Science

Program

Biomedical Engineering

Supervisor

Schmitz, Taylor W.

2nd Supervisor

Mur, Marieke

Co-Supervisor

Abstract

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder with limited early-stage treatment options. There is an urgent and unmet need for accurate biomarkers which can identify patients at risk for AD before cognitive symptoms emerge. Here I compared the performance of two analytical methods, univariate and multivariate classification, for identifying cognitively normal (CN) and mild cognitive impairment (MCI) patients based on their cerebrospinal fluid (CSF) biomarkers of Aβ42, pTau-181, sTREM2. Post-hoc analyses were then employed to assess patient progression in each of the SNF clusters. I found that SNF identified subgroups within the CN and MCI cohorts, based solely on conjoint patterns of CSF, uncaptured by univariate strategies. In both CN and MCI, a fast progressor patient cluster was identified. Our findings suggest that multivariate modeling of CSF data can uncover predictive patterns of AD progression which may help to stratify patients in clinical trials of preventative therapeutics.

Summary for Lay Audience

Alzheimer's disease (AD) affects over 50 million people globally, yet reliable pre-symptomatic diagnosis is lacking. Current treatments focus on symptom management during advanced stages of the disease. To address this, low-cost sensitive biomarkers are needed to identify individuals at risk before symptoms appear, akin to cholesterol levels and heart disease. Such proactive care strategies are currently absent in AD. The objective of my thesis is to evaluate whether warning signals in another type of bodily tissue, known as cerebrospinal fluid (CSF), can alert clinicians to risk for future AD, even when the patient has no signs of dementia. CSF is a liquid that bathes the brain and spinal cord and can be reliably and safely collected from the spine. It has proven to be one of the most effective tools for early detection, because it is in direct contact with the brain making it highly sensitive to abnormal biological processes in the brain. Biological markers, known as biomarkers, can be extracted from the CSF and measured to give us insight into the biological processes occurring in the brain. Over time, these biomarkers interact in ways that can be compared to the performance of an orchestra. In an orchestra, each musician (CSF biomarker) contributes to the overall performance (disease), and the complexity of the music comes from their coordinated interaction. Similarly, understanding the dynamics of AD involves considering each biomarker not just individually, but also in their interplay over time.

Previous research analyzed these biomarkers individually and at one point in time. In this thesis, we employed a new technique to understand this 'biomarker orchestra' by tracking multiple CSF biomarkers together over the course of multiple years. With this approach, iv we identified a pattern of biomarkers that can determine patients who are at high risk of developing AD, even before clinical symptoms emerge. We hope these analysis techniques can be used in combination with CSF biomarkers to identify high risk cognitively normal older adults who can then be enrolled in clinical trials to evaluate drugs for slowing or even preventing AD progression, akin to strategies in the fields of cardiovascular medicine.

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