Predicting onset of symptomatic Alzheimerʼs disease with plasma p-tau217 clocks

Predicting not just if, but also when, cognitively unimpaired individuals are likely to develop onset of Alzheimer's disease (AD) symptoms would be useful to clinical trials and, eventually, clinical practice. Although clock models based on amyloid and tau positron emission tomography have shown promise in predicting the onset of AD symptoms, a model based on plasma biomarkers would be more accessible. Using longitudinal plasma %p-tau217 (the ratio of phosphorylated to non-phosphorylated tau at position 217) from two independent cohorts (n = 258 and n = 345), clock models were used to estimate the age at plasma %p-tau217 positivity. The estimated age at plasma %p-tau217 positivity was associated with the age at onset of AD symptoms (adjusted R<sup>2</sup> of 0.337-0.612) with a median absolute error of 3.0-3.7 years. Notably, the time from %p-tau217 positivity to onset of AD symptoms was markedly shorter in older individuals. Similar models were constructed with data from one p-tau217/Aβ42 immunoassay and four plasma p-tau217 immunoassays. These findings suggest that the time until onset of AD symptoms can be estimated using a single blood test within a margin of error that is acceptable for use in clinical trials.

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Publication Details

Journal
Nature Medicine
Published
2026-02-19
DOI
https://doi.org/10.1038/s41591-026-04206-y
Citations
3
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
99.37
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article

Predicting onset of symptomatic Alzheimerʼs disease with plasma p-tau217 clocks

Benjamin Saef, L. Shaw, Lianlian Du, Anthony W. Bannon et al.
3 citations
Nature Medicine
Dementia and Cognitive Impairment Research
99.37
article

Predicting onset of symptomatic Alzheimerʼs disease with plasma p-tau217 clocks

Benjamin Saef, L. Shaw, Lianlian Du, Anthony W. Bannon, Kyle Ferber, Janaky Coomaraswamy, Kellen Petersen, DO Eric McDade, Lei Du‐Cuny, Marta Milà-Alomà, Ziad S. Saad, Carrie Rubel, Gallen Triana-Baltzer, Yulia Mordashova, J. Martin Sabandal, William Z. Potter, Yan Li, Nicholas J. Ashton, Erin G. Rosenbaugh, Michael Baratta, Alzheimer’s Disease Neuroimaging Initiative (ADNI), Emily A. Meyers, David M. Holtzman, Chengjie Xiong, Henrik Zetterberg, John C. Morris, Suzanne E. Schindler, Randall J. Bateman, Jeffrey L. Dage, Carlos Cruchaga, Duygu Tosun
article en
3 citations

Abstract

Predicting not just if, but also when, cognitively unimpaired individuals are likely to develop onset of Alzheimer's disease (AD) symptoms would be useful to clinical trials and, eventually, clinical practice. Although clock models based on amyloid and tau positron emission tomography have shown promise in predicting the onset of AD symptoms, a model based on plasma biomarkers would be more accessible. Using longitudinal plasma %p-tau217 (the ratio of phosphorylated to non-phosphorylated tau at position 217) from two independent cohorts (n = 258 and n = 345), clock models were used to estimate the age at plasma %p-tau217 positivity. The estimated age at plasma %p-tau217 positivity was associated with the age at onset of AD symptoms (adjusted R<sup>2</sup> of 0.337-0.612) with a median absolute error of 3.0-3.7 years. Notably, the time from %p-tau217 positivity to onset of AD symptoms was markedly shorter in older individuals. Similar models were constructed with data from one p-tau217/Aβ42 immunoassay and four plasma p-tau217 immunoassays. These findings suggest that the time until onset of AD symptoms can be estimated using a single blood test within a margin of error that is acceptable for use in clinical trials.

Nature MedicineVol. 32(3)
Alzheimer's Association (US), Rush University Medical Center (US), Biogen (United States) (US), Johnson & Johnson (United States) (US), University of Wisconsin–Madison (US), University of California, San Francisco (US), Washington University in St. Louis (US), Foundation for the National Institutes of Health (US), Sahlgrenska University Hospital (SE), AbbVie (United States) (US), Hope Center for Neurological Disorders (US), UK Dementia Research Institute (GB), AbbVie (Germany) (DE), Prevent Alzheimer’s Disease 2020 (US), Takeda (United States) (US), Banner Sun Health Research Institute (US), National Hospital for Neurology and Neurosurgery (GB), Northern California Institute for Research and Education (US), Banner Alzheimer’s Institute, Indiana University School of Medicine, University College London (GB), Rush University (US), Indiana University – Purdue University Indianapolis (US), University of Pennsylvania (US), University of Gothenburg (SE)
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Dementia and Cognitive Impairment Research
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