Scientists at Washington University School of Medicine in St. Louis have developed a blood test that estimates when Alzheimer's symptoms may begin, using levels of the protein p-tau217. The model predicts onset within about three to four years, potentially aiding clinical trials and early interventions. This advance relies on data from 603 older adults in ongoing studies.
Researchers from Washington University School of Medicine in St. Louis published findings on February 19 in Nature Medicine, detailing a predictive model based on a single blood test. The test measures p-tau217, a protein in plasma that reflects the buildup of amyloid and tau in the brain, hallmarks of Alzheimer's disease that accumulate years before symptoms appear.
The study drew from 603 older adults enrolled in the Knight Alzheimer Disease Research Center and the Alzheimer's Disease Neuroimaging Initiative. In one group, p-tau217 was assessed using PrecivityAD2, a test from C2N Diagnostics, a university startup. The other group used FDA-cleared tests from different companies. The model estimates the age at which symptoms might start, with a margin of three to four years.
Age influences the timeline: for someone with elevated p-tau217 at age 60, symptoms typically emerge about 20 years later, compared to roughly 11 years if elevation occurs at age 80. "Amyloid and tau levels are similar to tree rings -- if we know how many rings a tree has, we know how many years old it is," explained lead author Kellen K. Petersen, PhD, an instructor in neurology at the university.
Currently, more than 7 million Americans live with Alzheimer's, with care costs projected to reach nearly $400 billion in 2025, according to the Alzheimer's Association. The research, part of the Foundation for the National Institutes of Health Biomarkers Consortium, highlights blood tests as cheaper and more accessible than brain scans or spinal fluid analysis.
"Our work shows the feasibility of using blood tests... for predicting the onset of Alzheimer's symptoms," said senior author Suzanne E. Schindler, MD, PhD, an associate professor in neurology. The team has released the model code publicly and created a web application for further exploration, aiming to refine predictions for clinical use and efficient trials.
