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$27.2 Million National Effort Launches to Unify Alzheimer’s Research Data

Penn LDI Senior Fellow Yong Chen Is an MPI in the 10-Institution NIA Undertaking

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Yong Chen, PhD, LDI Senior Fellow and Professor at the Perelman School of Medicine, is a multiple principal investigator in a just-awarded $27.2 million, 10-institution National Institute on Aging (NIA) initiative to establish a collaborative network and data ecosystem to accelerate discovery and improve prevention, detection, and treatment strategies for Alzheimer’s disease and related dementias.

The overall initiative is led by UTHealth Houston and includes research teams from the University of Pennsylvania, Mayo Clinic, Rush University, Indiana University, University of Washington, University of Alabama at Birmingham, University of Florida, the University of Texas at San Antonio, and Vanderbilt University.

Yong Chen, PhD

Chen is a Professor of Biostatistics and Founding Director of Penn’s Center for Health AI and Synthesis of Evidence (CHASE), where he leads research in clinical evidence generation and synthesis using real-world data. He also directs the Penn Computing, Inference, and Learning (PennCIL) Lab, focusing on developing methods for integrating clinical data.

Alzheimer’s disease affects more than 7 million Americans age 65 and older, a number projected to increase sharply in the coming decades, according to the Alzheimer’s Association. The disorder is a devastating neurodegenerative condition that impacts not only patients but also their families, caregivers, and society as a whole.

Real-World Data

The five-year NIA “Using Real-World Data to Derive Common Data Elements for Alzheimer’s Disease and AD-Related Dementias Research Through Ontological Innovation” (ReCARDO) project focuses on coherently harnessing and standardizing real-world Alzheimer’s data from across the country. That information from everyday sources — such as electronic health records, insurance claims, mobile apps, and wearable devices — is increasingly essential for researchers seeking to understand how treatments and health patterns unfold in real-world settings.

Chen explained that currently, research data on Alzheimer’s disease and related dementias is scattered across many different studies, hospitals, and databases, each using its own formats and standards. This makes it extremely difficult for scientists to combine and compare information, slowing progress toward better prevention, diagnosis, and treatment. Even though vast amounts of real-world data already exist from electronic health records, insurance claims, genetics, imaging, and wearable devices, they often can’t be used together effectively because they aren’t aligned or compatible. As a result, valuable insights remain locked away in separate systems.

The ReCARDO project aims to fix this problem by creating a unified national research network that connects and standardizes Alzheimer’s-related data. The team will develop new Common Data Elements (CDEs), shared data standards and definitions that allow researchers to meaningfully link and analyze information from many sources.

Harmonized, Reliable, Reusable Data

Using advanced tools in artificial intelligence, machine learning, and natural language processing, ReCARDO will make it easier to extract, organize, and interpret data that were previously difficult to use together. The goal is to build a powerful data ecosystem where scientists across the country can securely access harmonized, reliable, and reusable information about Alzheimer’s disease.

“This collaborative infrastructure will make research faster, more transparent, and more reproducible, leading to stronger evidence and new discoveries,” Chen said. “Ultimately, this work focuses on speeding up the development of life-changing strategies for detecting, managing, and treating Alzheimer’s and related dementias.”

Chen’s team will contribute to the identification of key research questions, evaluate the feasibility and reproducibility of studies, and advance common data element development.

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