# Identification of proteins in plasma lipoproteins as biomarkers to aid in diagnosing and predicting progression of Alzheimer's disease in older adults > **NIH NIH R21** · UNIVERSITY OF MINNESOTA · 2020 · $201,509 ## Abstract Project Summary Currently, no effective strategies to prevent or slow AD exist, largely due to the lack of a complete understanding of the mechanisms that contribute to AD pathophysiology. While it is known that plasma lipoproteins regulate blood-brain barrier (BBB) integrity and modulate neuroinflammation, no studies have examined altered distributions of proteins among plasma lipoproteins as pro-inflammatory mechanisms underlying BBB integrity and neuroinflammation that may contribute to amyloid deposition, neurodegeneration, and cognitive decline in AD. The objective of the proposed study is to identify protein biomarkers in plasma lipoproteins that are indicative of abnormal amyloid deposition in the brain and are predictive of cognitive decline in community-dwelling older adults. This study will employ already-collected plasma samples and neuroimaging data and soon-to-be-collected cognitive decline data from the Atherosclerosis Risk in Communities-Neurocognitive Study (ARIC-NCS), a longitudinal cohort study. The central hypothesis is that the pro-inflammatory propensity of fractionated plasma lipoproteins, indicated by lower complement C3, alpha 2 macroglobulin (A2M), apolipoproteins D and E (Apo D and E), haptoglobin (HPT), and S100-A9, correlates with amyloid deposition, neurodegeneration, and cognitive decline. Plasma samples will be analyzed from 166 eligible ARIC-NCS participants without dementia who had baseline plasma samples collected, cortical thickness and brain amyloid deposition measured by neuroimaging, and cognitive function evaluated twice (baseline and five years later). A sequential gradient ultracentrifugation will be used to fractionate these plasma samples each to produce four plasma lipoproteins and a mass spectrometry-based targeted proteomics method will be used to measure the six selected proteins in fractionated plasma lipoproteins. Additional proteins will be evaluated to allow capacity for discovery. The specific aims are: Aim 1) determine proteins in plasma lipoproteins as biomarkers indicative of abnormal amyloid deposition in the brain. A case-control study design will be used to examine protein level differences between 30 participants with abnormal and 30 participants with normal amyloid deposition (carefully matched in categories defined by age, sex, and race); and Aim 2) evaluate proteins in plasma lipoproteins as biomarkers predictive of cognitive decline over time. A cohort study design of 60 participants with abnormal amyloid deposition will be used to correlate protein level data in a primary longitudinal analysis with cognitive decline over five years and in a secondary cross-sectional analysis with cortical thickness. This approach cost-effectively leverages already-collected plasma samples and neuroimaging data and soon-to-be collected cognitive decline data from the ARIC-NCS study. The proposed study will discover novel proteins in plasma lipoproteins that contribute importantly to AD pathophysiology... ## Key facts - **NIH application ID:** 9863994 - **Project number:** 5R21AG061372-02 - **Recipient organization:** UNIVERSITY OF MINNESOTA - **Principal Investigator:** Danni Li - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $201,509 - **Award type:** 5 - **Project period:** 2019-02-15 → 2022-01-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9863994 ## Citation > US National Institutes of Health, RePORTER application 9863994, Identification of proteins in plasma lipoproteins as biomarkers to aid in diagnosing and predicting progression of Alzheimer's disease in older adults (5R21AG061372-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/9863994. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*