The researchers at the Washington University School of Medicine in St. Louis have developed a methodology of determining when an individual is likely to start exhibiting symptoms of the Alzheimer disease through a single blood examination.
The team reported that in studies published today (February 19) in Nature Medicine) their predictive models could have predicted the time of symptom onset in an estimated three to four years. Such degree of accuracy might enable researchers to come up with more focused and quicker clinical trials that prevent Alzheimer therapies. It can even end up assisting doctors to find individuals who will benefit most of the treatment prior to the development of memory problems.
Alzheimer is afflicting over 7 million Americans. The Alzheimer Association estimates that the cost of caring to those with Alzheimer and other dementias will reach almost 400 billion in 2025. Despite the fact that a cure is yet to be developed, the instruments that predict the onset of the symptoms may enhance the attempts at delaying or preventing the occurrence.
The ability to predict the appearance of the symptoms of Alzheimer using blood tests, which are much more affordable and more available than brain scans or spinal fluid tests, is demonstrated in our work, said its senior author Suzanne E. Schindler, MD, PhD, an associate professor in the WashU Medicine Department of Neurology. She further said that these models would reduce time taken to assess preventive therapies during clinical trials.
She said that, in the near term, these models will shorten our research and clinical trials. The ultimate aim is to be in a position in the future to inform each patient when they are likely to get symptoms which will assist them and their physicians in coming up with a strategy on how to avoid the situation or delay the symptoms.
The Alzheimer Clock and p-tau217 Protein.
The study has been carried out as a project initiated by the National Institutes of Health Biomarkers Consortium a public-private alliance which also encompasses WashU Medicine.
The methods of prediction are based on the fact that it is possible to measure a protein, p-tau217, in the plasma, the liquid component of blood. Through the research, the researchers are able to determine the age at which an individual might begin having signs of Alzheimer onset by examining concentrations of this protein. Nowadays, p-tau217 testing is utilized in the diagnosis of Alzheimer in individuals with cognitive impairment. It should not be used in individuals who do not experience any symptoms outside the research or clinical trials.
To establish the median duration between the onset of symptoms following an increase in p-tau217 levels, Schindler and senior author Kellen K. Petersen, PhD, an instructor in the Neurology department at WashU Medicine, analyzed the data of 603 people living independently. Two long term studies, the WashU Medicine Knight Alzheimer Disease Research Center (Knight ADRC) and the Alzheimer Disease Neuroimaging Initiative (ADNI) running at various sites across the U.S. were used to recruit participants.
Confirmed In Repeated Blood Tests of Alzheimer.
PrecitivityAD2 is a commercially available Alzheimer blood test that was developed by C2N Diagnostics and was used to measure plasma p-tau217 in the Knight ADRC group. The company C2N is a WashU startup, co-founded by David M. Holtzman, MD, the Barbara Burton and Reuben M. Morriss III Distinguished Professor and Randall J. Bateman, MD, the Charles F. and Joanne Knight Distinguished Professor of Neurology at WashU Medicine. The two are coauthors of the study. The p-tau217 levels in the ADNI group were analyzed with the tests of other companies including the one approved by the U.S. Food and Drug Administration.
Past studies have indicated that plasma p-tau217 is a close indicator of the build up of amyloid and tau in the brain based on PET images. Among the distinguishing characteristics of the Alzheimer disease, there are amyloid and tau that build up years prior to the start of memory impairments.
The level of Amyloid and Tau is comparable to the tree rings, as Petersen explained, once we know the number of rings in a tree we know the number of years that it had lived. It turns out that, amyloid and tau also deposition happen in an identical pattern and their age of becoming positive is a strong predictor of when a person will develop the symptoms of Alzheimer. This is also true of plasma p-tau217 which is a measure of amyloid and tau levels.
Within Three to four years.
The models projected the age of onset of the symptoms with an average error of three to four years. The results also indicated that age is an issue. The elderly were more likely to develop the symptoms earlier than their p-tau217 levels increased in comparison with younger people. This trend is indicative of the possibility that younger brains would be capable of withstanding Alzheimer-related changes to a longer degree, whereas the elderly would have symptoms at a lower level of underlying pathology.
Indicatively, a person who has had p-tau217 levels increased at the age of 60 began showing signs approximately 20 years later. Conversely, an individual whose levels increased at 80 years of age usually manifested the symptoms in 11 years.
The researchers additionally ensured that their model had been strong within different p-tau217-based tests in occasions other than PrecivityAD2, an assurance of the strength and expanded applicability of the model.
The team published their code of development of model as a whole to motivate other people to work in this field. Petersen additionally developed a web-based application in which other scientists can access the more-vast predictive clock models and put them to test.
Petersen said these clock models would be useful in enabling clinical trials become more efficient because they can help identify individuals who are prone to developing symptoms within a given time. These methodologies can predict the development of symptoms reliably enough in the future to apply it to individual clinical care with further refinement.
He observed that other blood biomarkers are also associated with cognitive deterioration in the Alzheimer disease. Further studies can involve the use of more markers to enhance the accuracy of symptoms onset predictions.
Journal Reference
Petersen KK, Mila-Alomana M, Li Y, Du L, Xiong C, Tosun D, Saef B, Saad ZS, Du-Cuny L, Coomaraswamy J, Mordashova Y, Rubel CE, Meyers EA, Shaw LM, Dage JL, Ashton NJ, Zetterberg H, Ferber K, Triana-Baltzer G, Baratta M, Rosenbaugh EG, Cruchaga C, McDade E, Holtzman DM, Morris Prognosis of symmetomatic Alzheimer disease using a plasma plasma 217 per cent tau clock. Nature Medicine. Feb. 19, 2026. DOI: 10.1038/s41591-026-04206-y
Funding and Data Sources
These results are based on the Foundation of the National Institutes of Health (FNIH) Biomarkers Consortium Project, Plasma A 2 and Phosphorylated Tau as Predictors of Amyloid and Tau Positivity in Alzheimer’s Disease. Industry, academic, patient advocacy, and government partners provided financial and scientific support of the work. The abstract of the full press release announced that financing was done by AbbVie Inc., Alzheimer’s Association, Diagnostic Accelerator at the Alzheimer’s Drug Discovery Foundation, Biogen, Janssen Research and Development, LLC, and Takeda Pharmaceutical Company Limited. The foundation of the National Institutes of Health managed the private sector funding.
Biomarkers Consortium, Plasma A2 and Phosphorylated Tau as Predictors of Amyloid and Tau Positivity in Alzheimer Disease Project was conducted by a public private partnership managed by the Foundation of the National Institute of Health (FNIH) and sponsored by AbbVie Inc, Alzheimer Association 2, Diagnostics Accelerator at Alzheimer Foundation Drugs Discovery Foundation, Biogen, Janssen Research and Development, LLC and Takeda Pharmaceutical Company Limited. National Institute on Aging grant R01AG070941 supported the statistical analyses.
The information to come up with this article was sourced through the Alzheimer disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). In this regard, the researchers in the ADNI provided inputs to the design and execution of ADNI and or supplied information but were not involved in the analysis and writing of this report.
