Research News

A team led by Feixiong Cheng, PhD, Director of the Cleveland Clinic Genome Center, is developing a platform to analyze data associated with Alzheimer’s disease, inflammation and aging in our brains. Backed by a five-year, $4 million grant from the National Institutes of Health, the project seeks to identify biological aging processes in the brain that can be halted or reversed to prevent or delay Alzheimer’s disease. 

“Chronological age is the easiest and most obvious way to measure age, but we’ve all met people who seem younger or older than they really are due to their health,” Dr. Cheng explains. “Our brains also have their own biological age. The features that predispose our brains to developing Alzheimer’s disease primarily develop as our chronological age increases, but everyone develops these features on their own timeline.” 

How are Alzheimer’s disease and inflammation connected? 

One feature Dr. Cheng and his collaborators will focus on as they characterize biological aging in our brains is inflammation. Inflammation is closely linked to biological aging throughout the body, including the brain, Dr. Cheng says.  

“By analyzing patterns of inflammatory molecules in the blood, we can assess brain health relative to others of the same age and develop an inflammatory age (iAge) clock to predict someone’s biological age,” he says. 

In addition to age, inflammation in the brain is a significant contributor to Alzheimer’s disease development. Due to this overlap, Dr. Cheng and his collaborators need to understand how inflammation works in relation to, and independently from, age, to provide proactive brain care for individuals at risk of developing Alzheimer’s disease. 

Computational models are critical to examining the complex interplay between inflammation, aging and Alzheimer’s disease because of the ability to overlay and analyze separate, large data sets.  

“There is a growing mass of data on multiple levels – genetic, transcriptomic, proteomic, interactomic and even real-world patient data – that we can use to improve Alzheimer’s disease drug development and patient care,” Dr. Cheng says. “Unfortunately, we lack the computational models needed to effectively combine and analyze all this data.” 

Building a “toolkit” to understand the aging brain 

Dr. Cheng will work with Indiana University School of Medicine researchers Andrew Saykin, PsyD, whose team specializes in fluid biomarkers and multimodal neuroimaging, and Pengyue Zhang, PhD, who develops statistical models to analyze real-world patient data. The team will develop a computational platform to address these needs. The platform is called M3SB, and will provide researchers with a Multimodal, Multiscale, Multistage transformative Systems Biology infrastructure to use as a toolkit when studying aging and identifying effective Alzheimer’s disease treatments: 

• Multimodal: the platform will be capable of analyzing and integrating many different forms of datasets including genomics, transcriptomics, proteomics, metabolomics, interactomics, radiomics and real-world patient databases using advanced artificial intelligence (AI) technologies established by the Cheng lab.
• Multiscale: the platform will be trained using datasets from multiple species including humans and yeast, to identify the most important proteins and cellular features related to longevity that are conserved across the evolutionary tree
• Multistage: the platform will cover the different stages of disease progression, as well as the different ways the disease can manifest across lifestyles. 

To make sure their platform is as accurate as possible, Dr. Cheng and his collaborators will also use their award to comprehensively investigate molecular features of biological aging versus chronological aging. Their goal is to use M3SB to identify and target aging features associated with Alzheimer’s disease. 

The team will also use their award to develop what they call a PhenoAge Clock, which will use anonymized patient records (including fluid biomarkers and laboratory testing results) to identify factors that can be used to predict an individual’s biological age independently from inflammation and other biological changes in the body. 

“Combining a systematic understanding of the biology that drives aging with genetic factors and real-world evidence will serve as a foundation for our platform,” Dr. Cheng says. “We are excited to develop an AI toolkit that any researcher can use to identify and validate disease-modifying targets and treatments for brain aging and Alzheimer's disease."