Biomarkers for Parkinson’s disease and Alzheimer’s disease - Liu, B (2018)
Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) represent the two most prevalent neurodegenerative diseases in the world, affecting 30 million and 5 million people, respectively. As populations age, the global burden of AD and PD will increase, resulting in significant societal and economic implications. By 2050, it is estimated that 1 in 85 people will have AD. Biomarkers, surrogate indicators of physiological or pathophysiological states, can be used to guide the diagnosis of diseases, evaluate risk or prognosis, and track therapeutic interventions. In neurodegenerative diseases such as PD and AD, biomarkers can play a crucial role by facilitating earlier diagnosis and the screening of individuals into clinical trials. Given its function to track various disease states, biomarkers will be fundamentally important as we develop and assess disease-modifying therapies and preventative strategies. (3) The advent of high-throughput sequencing technologies and advancements in omics— genomics, proteomics, and transcriptomics—has enabled the exploration of biomarkers for disease with unprecedented scale. Investigators can now move beyond candidate biomarker discovery approaches based upon a priori hypotheses. This presents an opportunity to utilize biomarkers to develop more sensitive and specific diagnostics and identify molecular targets that may have been overlooked through candidate approaches. Researchers can also use biomarkers to better define subgroups within PD or AD. These endophenotypes can help characterize the different etiologies that contribute to the development of the diseases, reveal distinct subpopulations within disease categories, and thereby uncover potential therapeutic targets. The work presented here leverages these new advancements in high variable capture approaches, specifically proteomics, to identify novel biomarkers and signatures of disease states for PD and AD. My thesis also demonstrates how multivariate analytical tools can be used to interrogate and validate distinguishing signatures found between disease and control states. In the first chapter, I will review the pathophysiology and clinical management of AD and PD. I will also review the AD and PD biomarkers and the various proteomic and computational approaches that enable biomarker discovery at scale. In the second chapter, I introduce a novel biomarker discovery approach that combines the advantages of hypothesis-driven and high variable capture approaches. The third chapter explores biomarkers for PD, using data from plasma and brain. In the fourth chapter, I identify proteins correlated across CSF and plasma and demonstrate how this data may be used in biomarker identification. In summary, I have identified novel biomarker signatures for the diagnosis and prognosis of PD and AD using high variable data capture methods and multivariate computational approaches.