The Alzheimer-disease (AD) project

Photo by Toa Heftiba on Unsplash

The proportion of the US population aged 65 years and older is projected to increase significantly, from 16% to 22% by 2040. Additionally, there is an anticipated 118% rise in the number of individuals over the age of 85. This demographic shift highlights the urgent need for early diagnosis and innovative therapies to address the growing incidence of age-related conditions, notably Alzheimer’s disease (AD) and dementia. However, conducting clinical trials for these conditions presents unique challenges due to factors such as long-term disease progression and the presence of comorbid conditions. To overcome these obstacles, longitudinal observational cohorts can provide valuable insights.

In this project, we have outlined two key research activities. Firstly, the “Synthesis of Information from Multi-sourced Data for Prediction and Learning of Alzheimer’s Disease and Dementia” (SIMPLE-ADD) aims to utilize existing data resources, including the National Alzheimer’s Coordinating Center, the Alzheimer’s Disease Neuroimaging Initiative, and the Religious Orders Study/Memory and Aging Project (ROS, MAP). By leveraging these resources, we seek to enhance our understanding of disease mechanisms associated with AD and dementia.

Secondly, we will employ causal mediation analysis to investigate the extent and manner in which changes in imaging and neuropathology biomarkers mediate the causal effects of various exposures, such as drugs, Apoe, and other genetic mutations, on the development of AD and dementia. This analysis will provide insights into the underlying pathways through which these factors influence the onset and progression of these conditions.

Liangliang(Lyon) Zhang
Liangliang(Lyon) Zhang
Assistant Professor

Dr. Zhang’s research interests center around Bayesian inference and prediction, high dimensional models, and complex structured data, such as brain imaging and metagenomic data.