By Meghan McCarthy
Author’s Note: This article is part of ongoing coverage of the 2024 Alzheimer’s Association International Conference (AAIC). To view all highlights, please click here.
Recent research through the Departments of Bioengineering, Neurology, and Radiology at the University of Pennsylvania is exploring the biologic causes for atypical Alzheimer’s disease (AD). Led by Lasya Sreepada, MS, BS, a doctoral candidate at Penn’s School of Engineering and Applied Sciences, the team presented their work at the 2024 AAIC.
Atypical AD affects approximately 15 percent of AD patients. The term encompasses individuals who experience cognitive outcomes that differ from typical AD symptoms. For example, individuals with atypical AD may experience greater change in their behavior, personality, and language compared to symptoms predominantly based around memory. Usually, atypical AD patients also develop their symptoms at a younger age.
Atypical AD highlights the heterogeneity in Alzheimer’s disease and related dementias (ADRD), or the variance in disease symptoms and presentation.
Sreepada’s work aims to understand ADRD heterogeneity.
Lasya Sreepada
“We are trying to understand why this disease is so different across patients,” Sreepada said. “We want to understand the biology that is driving these differences so that we can better manage patient care as well as develop target individualized treatments that address specific patient concerns.”
At this year’s AAIC conference, Sreepada presented work focused on understanding atypical AD through comparing patients’ chronological and biological aging.
Scientists like Sreepada can measure age with more data than just a birthdate. Biological aging accounts for the various experiences in life that may slow or speed up the way your body ages. For example, a man born in 1945 would have the chronological age of 79. However, depending on his lifestyle and environment, his biological age may be greater or less than 79.
The concept of biological aging falls into the bracket of epigenetics, or how environmental factors influence the way genes are expressed in the body.
“Genes control how your body grows, develops, and ages,” Sreepada said. “Epigenetics is the layer on top of that, and controls which genes are turned on and off at different points of life. It is affected by both hereditary and environmental factors.”
In her study, Sreepada used participant data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Her team compared a patient’s chronological age to their biological age to see if there was a biological age gap (BAG), or difference between the two.
To first identify which patients demonstrated atypical AD, they looked at the thickness of specific areas of the brain, known as the medial temporal lobe (MTL) and the cortex. To analyze thickness, Sreepada and her team created a machine learning algorithm known as the Cortico-Medical Temporal Index (COMeT). COMet is designed to detect and measure atypical AD patterns within a given patient.
To determine biological age, she analyzed DNA methylation within these regions. DNA methylation occurs when small chemical groups tag, or latch, onto DNA. To detect and measure this, Sreepada utilized another tool known as epigenetic clocks.
Epigenetic clocks are algorithms trained to predict biological age based on DNA methylation from individuals.
“Methylation is a type of epigenetic marker that controls which genes are turned on or off, so we use DNA methylation to compute biologic age,” Sreepada said.
Using the CoMET and Epigenetic Clocks, her team compared biological aging in patients with typical and atypical AD.
She found that patients who were biologically younger had lower COMeT scores, indicating greater AD atypically. This means that biologically younger patients are more likely to present atypically than biologically older patients.
These results suggest that biological aging mechanisms impact atypical disease. Thus, an individual of younger chronological age but with greater than normal biological aging may be more likely to develop atypical AD.
These findings highlight the importance of considering biological aging within ADRD precision medicine.
Ultimately, Sreepada looks forward to replicating this study in larger, more diverse data sets that have better representation.
“I am really excited about exploring the concept of biological aging to study other types of dementias and other neurological diseases broadly,” Sreepada said. “I am very interested in the concept of healthy aging and seeing what we can do in terms of suggesting lifestyle changes to encourage healthier aging or perhaps even prevent some of these devastating diseases.”
Lasya Sreepada is a doctoral candidate at the School of Engineering and Applied Sciences at Penn. To learn more about her background, please click here.
To read about biological vs chronological aging comparisons based on race and ethnicity, please click here.