2023 Alzheimer's Association Research Grant (AARG)

Uncovering Immune Mechanisms and Biomarkers of ARIA (UNIMIB-ARIA Toolkit)

What biological indicators predict the onset of a side effect of amyloid-clearing therapies?

Fabrizio Piazza, Ph.D.
University of Milano-Bicocca (Università degli Studi di Milano-Bicocca)
Milano, Italy

Background

Beta-amyloid is a sticky protein fragment that forms toxic clumps called plaques in the brain, a key hallmark of Alzheimer’s. Researchers have worked to develop a therapy to target these amyloid clumps, including using antibodies recognized by the body’s immune system to induce the immune system to remove the beta-amyloid plaques. Some of the anti-amyloid therapies have  shown amyloid-clearing abilities in clinical trials and have been approved by the FDA. However, these therapies have been shown to also contribute to adverse side effects known as amyloid-related imaging abnormalities (or ARIA). One type of ARIA, called ARIA-E (ARIA-Edema), is characterized by accumulation of fluid in the brain and side effects include changes in mental state, seizures, tremor, headache and nausea.
 
The greatest risk factor for ARIA-E is a condition called cerebral amyloid angiography related inflammation (CAA-ri), but ARIA-E is mainly driven by the inflammation linked to beta-amyloid antibodies in the cerebral spinal fluid (CSF; the biological fluid surrounding the brain and spinal cord). In initial studies, Dr. Fabrizio Piazza and colleagues found that an increased amount of beta-amyloid antibodies in the CSF were strongly related to the occurrence of  ARIA-E. By identifying these kinds of biological changes (or biomarkers) of ARIA, they can better understand the disease and make predictions about who is most at risk for developing it is important in this new era of Alzheimer’s treatment.

Research Plan

Dr. Piazza and team plan to identify biomarkers underlying ARIA development by gathering blood and CSF samples from individuals with ARIA-E, non-inflammatory CAA, or who are otherwise healthy to  identify who is most at risk for developing ARIA-E. They also hope to identify when after symptom onset is the optimal time to take blood and CSF samples in order to find biomarkers that might inform ARIA treatment.

Impact

The study results could shed new light on the mechanisms that underlie the development and progression of ARIA. They could also lead to a better identification of who is most at risk for developing ARIA, and how to best proceed with ARIA treatment.