Episode 67: Tiny particles offer big clues toward predicting Alzheimer’s decades in advance

TGen Talks

Alzheimer’s disease affects an estimated six million Americans. Diagnosing and treating the disease is challenging, and for families taking care of a loved one with Alzheimer’s, it’s even more difficult. Detecting and addressing the disease early on is crucial due to its progressive nature. However, Alzheimer’s symptoms can resemble those of other non-progressive conditions. In a recent Cells publication, a team of scientists describe using machine learning models to identify changes in RNA molecules of plasma extracellular vesicles (EVs) that may hold potential for identifying Alzheimer’s disease (AD) at its earliest stages. This is one of the first studies to show changes in the RNA molecules of plasma EVs that precede neurodegeneration and provides evidence that some of the hidden pathology taking place early in the disease is reflected in plasma EVs, where it can be accessed in a minimally invasive manner and used for biomarker development. On this edition of TGen Talks, study co-author and TGen Neurogenomics Division staff scientist Joanna Palade, Ph.D., discusses their findings, and how what sound like magic or a fortune teller's promise, is the goal of the scientists working to develop a simple test; one that wouldn't simply indicate whether your symptoms might progress to an Alzheimer's diagnosis, but could also estimate the timeframe for when it might occur.