EVALUATING THE RELATIONSHIP BETWEEN PLASMA BIOMARKERS AND DEMENTIA USING HIERARCHICAL CLUSTERING ANALYSIS AND LINEAR MODELING
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Doctor of Philosophy (PhD)
Dr. Donna M. Wilcock
Dementia is a disorder characterized by a significant decline from baseline in one or more cognitive domains that interferes with independence. Prevalence of dementia worldwide is estimated at 50 million people, with that number expected to triple by 2030, coming with a cost of roughly $2 trillion. Clinically, dementia is diagnosed using cognitive evaluations, with varying domains affected and to different degrees depending on the underlying pathology and stage of disease. Alzheimer’s disease (AD) and vascular contributions to cognitive impairment and dementia (VCID) are the two leading causes of dementia, and both have pathologies which can be visualized using MRI. Additionally, protein quantification from cerebral spinal fluid (CSF) can be both diagnostic and prognostic for AD. However, the high costs of MRIs and the invasiveness of CSF draws limit their utility as screening tools for dementia. Therefore, we must look toward a more cost effective and less invasive screening tool, which leads us to plasma-based biomarkers. In this dissertation, I will discuss three experiments which examine the association between plasma-based biomarkers and dementia using hierarchical clustering analyses (HCA) and linear modeling.
In the first experiment, we compared two models of HCA to create plasma profiles of participants with mild cognitive impairment due to VCID. Both models identified a profile consisting of elevated VEGF-A, MMP1, MMP9, and IL-8, which suggests patients with this profile have an increased angiogenic and inflammatory state potentially coinciding with pathological progression. In the second experiment, we evaluated the association between plasma biomarkers and various dementia neuropathologies in an autopsy cohort of participants. In this study, we found that PlGF was positively associated with amyloid angiopathy, while IL-6 was inversely associated with more severe chronic vascular grade. Additionally, we found that VEGF-A was positively associated with Aβ plaque score, while Aβ42/40 was inversely associated with more severe AD pathology. These results demonstrate that increased angiogenesis is positively associated with worsening AD neuropathology and should be further studied in a larger longitudinal cohort. Lastly, we evaluated the relationship between plasma biomarkers and cognitive impairment in a longitudinal cohort of participants and found that 6-years post-baseline GFAP and NfL were associated with a decline in verbal memory and verbal fluency, respectively. Interestingly, the anti-inflammatory cytokine, IL-10, was found to be positively associated with both verbal memory and verbal fluency at both 3- and 6-years post-baseline. These results suggest that higher levels of neurodegenerative biomarkers at baseline may be predictive of long-term cognitive decline, while higher levels of anti-inflammatory cytokines at baseline may prove beneficial in preventing middle- and long-term cognitive decline.
Overall, we have shown how angiogenic and inflammatory plasma biomarkers have the potential to be used as prognostic indicators of both pathology and cognitive impairment. Moving forward, these markers will need to be validated in larger more generalizable cohorts through multi-center trials. The goal for these markers will be to use them in the clinic to facilitate the diagnosis of dementia and help physicians make more informed predictions about the progression of the disease.
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This study was supported by the National Institute on Aging Grant (no. UH3NS100606) in 2018-2022, National Institutes of Health T32 Fellowship Program Grant (no. T32AG057461) in 2020-2021, and National Institutes of Health F30 Ruth L. Kirschstein Individual Predoctoral NRSA for MD/PhD Fellowship Grant (no. F30 NS118777-01A1) in 2021-2022.
Winder, Zachary, "EVALUATING THE RELATIONSHIP BETWEEN PLASMA BIOMARKERS AND DEMENTIA USING HIERARCHICAL CLUSTERING ANALYSIS AND LINEAR MODELING" (2022). Theses and Dissertations--Physiology. 59.