Publications by authors named "Ekaterina Hofrenning"

Background: Vascular contributions to cognitive impairment and dementia (VCID) are often comorbid with Alzheimer's disease and increase the risk of dementia. Blood-based biomarkers may be promising for identifying individuals at high risk for VCID due to small vessel disease (SVD).

Method: We included 1709 participants from the Mayo Clinic Study of Aging who had concurrent MRI, plasma neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) measured on the HD-X Simoa Quanterix platform, and clinical dementia rating scale (CDR).

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There is increasing interest in Alzheimer's disease related plasma biomarkers due to their accessibility and scalability. We hypothesized that integrating plasma biomarkers with other commonly used and available participant data (MRI, cardiovascular factors, lifestyle, genetics) using machine learning (ML) models can improve individual prediction of cognitive outcomes. Further, our goal was to evaluate the heterogeneity of these predictors across different age strata.

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Objective: We examined the long-term effects of premenopausal bilateral oophorectomy (PBO) with or without concurrent or preceding hysterectomy on physical and cognitive function and on odds of chronic conditions.

Methods: We enrolled 274 women with PBO with or without concurrent or preceding hysterectomy and 240 referents aged 55 years and older who were residents of Olmsted County, MN as of the PBO or index date. Chronic conditions were assessed via medical record abstraction.

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Blood-based biomarkers offer strong potential to revolutionize diagnosis, trial enrolment and treatment monitoring in Alzheimer's disease (AD). However, further advances are needed before these biomarkers can achieve wider deployment beyond selective research studies and specialty memory clinics, including the development of frameworks for optimal interpretation of biomarker profiles. We hypothesized that integrating Alzheimer's disease genetic risk score (AD-GRS) data would enhance the diagnostic value of plasma AD biomarkers by better capturing extant disease heterogeneity.

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Background: The National Institute on Aging-Alzheimer's Association Research Framework proposes defining Alzheimer's disease by grouping imaging and fluid biomarkers by their respective pathologic processes. The AT(N) structure proposes several neurodegenerative fluid biomarkers (N) including total tau (t-tau), neurogranin (Ng), and neurofilament light chain (NfL). However, pathologic drivers influencing each biomarker remain unclear.

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The clinical usefulness MRI biomarkers for aging and dementia studies relies on precise brain morphological measurements; however, scanner and/or protocol variations may introduce noise or bias. One approach to address this is post-acquisition scan harmonization. In this work, we evaluate deep learning (neural style transfer, CycleGAN and CGAN), histogram matching, and statistical (ComBat and LongComBat) methods.

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The objective of this study was to determine the differential mapping of plasma biomarkers to postmortem neuropathology measures. We identified 64 participants in a population-based study with antemortem plasma markers (amyloid-β [Aβ] x-42, Aβx-40, neurofilament light [NfL], and total tau [T-tau]) who also had neuropathologic assessments of Alzheimer's and cerebrovascular pathology. We conducted weighted linear-regression models to evaluate relationships between plasma measures and neuropathology.

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: Plasma glial fibrillary acidic protein (GFAP) may be associated with amyloid burden, neurodegeneration, and stroke but its specificity for Alzheimer's disease (AD) in the general population is unclear. We examined associations of plasma GFAP with amyloid and tau positron emission tomography (PET), cortical thickness, white matter hyperintensities (WMH), and cerebral microbleeds (CMBs). : The study included 200 individuals from the Mayo Clinic Study of Aging who underwent amyloid and tau PET and magnetic resonance imaging and had plasma GFAP concurrently assayed; multiple linear regression and hurdle model analyses were used to investigate associations controlling for age and sex.

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