P-Tau217 Clock Predicts Alzheimer’s Progression Over 30 Years

Series - Tau2022 Global Conference (Virtual Meeting):

Researchers need better tools to predict when people with preclinical Alzheimer’s pathology are likely to develop symptoms. At the Tau2022 conference, held virtually February 22-23, Suzanne Schindler at Washington University in St. Louis put forward a p-tau217 “clock” as one option. She analyzed the longitudinal change in p-tau217 cerebrospinal fluid levels in older adults, and found that after p-tau217 passes a “tipping point,” it rises at a consistent rate in everyone for the next 30 years. The age at which people reached this tipping point predicted the age when they would develop symptoms, with about the same accuracy as familial age of onset in autosomal dominant AD.

  • Once plaques start forming, CSF p-tau217 increases at the same rate in everyone.
  • A p-tau217 “clock” can predict the age of symptom onset.
  • This clock starts six years earlier, and runs longer, than the PiB PET amyloid clock.

Schindler believes this p-tau217 clock could help researchers select secondary prevention trial participants with sporadic AD who are close to developing symptoms, thus improving trial power and efficiency. “P-tau217 seems to be a very early marker that is useful for a long time,” she told Alzforum.

Jeffrey Dage at Indiana University School of Medicine, Indianapolis, called the findings encouraging, and agreed there could be clinical trial applications. “If the relationship between biomarker level and time to AD symptom onset is influenced by treatment predictably and reliably in broad, diverse populations, then these measures could potentially serve as a useful way to study the effects of amyloid-removing therapies in preclinical AD,” he wrote (full comment below).

Disease Takes Off. In people who do not yet have AD, longitudinal CSF p-tau217 measurements (vertical lines) fluctuate up and down from the baseline number (horizontal line). However, after p-tau217 reaches a “tipping point” of 3.7 percent of total 217 tau fragments, the values only climb. Dotted line indicates a positive PiB PET scan, and colors reflects CSF Aβ42/40 values (blue-purple are normal, yellow-red abnormal). [Courtesy of Suzanne Schindler.]

Several studies have found that amyloid accumulates at a predictable rate (Jack et al., 2013; Villemagne et al., 2013; Oct 2019 news). Previously, Schindler had developed an “amyloid clock,” devising a way to predict the time of symptom onset from a single PiB PET scan (Sep 2021 news). In her latest work, she wanted to expand this approach to fluid biomarkers.

To build a tau clock, Schindler turned to CSF data from 385 participants in longitudinal studies at the Knight Alzheimer's Disease Research Center that had been generated by Nicolas Barthélemy and Randall Bateman at WashU. Using mass spectrometry, Barthélemy previously determined that rising CSF p-tau217 and p-tau181 reflected the growth of amyloid plaques in the brain, while other markers such as p-tau205 rose later and correlated with other aspects of disease (Mar 2020 news). Others have found similar relationships by immunoassay of plasma and CSF (Aug 2019 conference news; Jul 2020 conference news).

Schindler analyzed these longitudinal data to find the threshold level of each p-tau marker that heralded disease onset. In people without amyloid plaques, the percent phosphorylation fluctuated over time with no set pattern. However, after a certain tipping point, each p-tau climbed steadily, indicating the person was now on the path to AD. This occurred at a different age in each person. P-tau217 best mirrored the changes in the Aβ42/40 ratio, indicating a close relationship with plaques.

Notably, while the Aβ ratio dropped linearly after its tipping point, p-tau217 shot up exponentially after it reached its threshold, which occurred at about 3.7 percent phosphorylation (see image above). This may reflect a biological difference between the markers, Schindler told Alzforum. Aβ drops because the peptide is being sequestered in the brain, and thus depleted from CSF. P-tau217, on the other hand, is produced by the brain’s response to amyloid plaques. Injury responses often display an exponential pattern of change, Schindler noted.

To turn these exponential data into a p-tau217 clock that keeps linear time, Schindler morphed the data with logarithms. Analyzing the WashU data by the log of the p-tau217 concentration, she found the clock maintained a steady rate of change over a period of 30 years, longer than the 18 years of the PiB PET amyloid clock (see animation below). Likely, this is because amyloid load plateaus around the time of symptom onset, while p-tau keeps rising.

From Chaos, Order. Aligning people's p-tau217 trajectories by their chronological age reveals no clear pattern, but anchoring them by the p-tau217 tipping point reveals a consistent rate of change for nearly everyone. [Courtesy of Suzanne Schindler.]

The beauty of this clock is that a person’s p-tau217 level at any time could be used to estimate where they are on this trajectory and, by extension, at which chronological age they reached the tipping point. This typically came about six years earlier than the PiB PET tipping point. This may be because brain-wide PiB PET positivity is not a very sensitive measure, Schindler noted. Regional plaques have already grown before the global scan turns positive. She believes the p-tau217 tipping point coincides with these initial regional amyloid deposits.

While the p-tau217 clock started earlier and ran longer than the PiB PET amyloid clock, it was not quite as accurate. In this dataset, 48 people progressed from cognitively healthy to AD dementia; in them, the p-tau217 tipping point predicted their age at symptom onset with a correlation of 0.66 and a variance of plus or minus five years. This is fuzzier than the PiB PET clock estimate, which varies by plus or minus three years. However, it is about the same as estimated year of onset (EYO) predictions based on mutation type or parental year of onset in autosomal-dominant AD.

Oskar Hansson at Lund University, Sweden, was intrigued by the evidence of a variable tipping point but a steady rate of change thereafter. “It implies to me that genetics and lifestyle factors mainly affect when the tipping point is reached in a certain individual, but once the aggregation process of Aβ has started in widespread cortical areas, the speed of continued aggregation is constant and not affected to a large degree by different risk or protective factors,” he wrote (full comment below).

In future work, Schindler will further analyze cognitive data to nail down the relationship between the p-tau217 tipping point, age, and symptom onset. She will also examine plasma p-tau217 to see if the relationship holds there, as a blood test would make a more clinically useful biomarker.—Madolyn Bowman Rogers

 

Comments

    • User Profile Image Jeffrey Dage
      Indiana University School Of Medicine
    • Posted:

    At the Tau 2022 conference, during the Biomarkers and Diagnosis session on “evaluating the trajectories of fluid biomarkers,” Suzanne Schindler expertly described the accumulation of amyloid in the preclinical stage of AD and showed the consistency of the biological effect independent of APOE genotype and age. Although on a small dataset (n=19 for PET), she extended these findings to show a strong association with AD symptom onset. Dr. Schindler then shared a similar set of analyses with CSF measures of pTau217, using a mass spectrometry measurement technique that allowed the calculation of a phosphorylation site occupancy ratio on the pTau217 peptide and was also able to determine an association with the age of AD symptom onset.

    Although Dr. Schindler presented on small initial datasets (n=19 for PET and n=48 for CSF pTau217/Tau217) and included only the positive subset of progressors in the analyses, they are very encouraging findings. These results strongly support the need for long-term and diverse studies in preclinical AD, where longitudinal biomarker measurements are included to prospectively evaluate the use of biomarkers for predicting AD symptom onset. If the relationship between biomarker level and time to AD symptom onset is influenced by treatment predictably and reliably in broad diverse populations, then these measures could potentially serve as a useful way to study the effects of amyloid removing therapies in preclinical AD.

    View all comments by Jeffrey Dage

  2. The work by Suzanne Schindler and others, showing that the rates of accumulation of amyloid fibrils (as measured with longitudinal PET) are consistent between individuals, is very intriguing. It implies to me that genetics and lifestyle factors mainly affect when the tipping point is reached in a certain individual, but once the aggregation process of Aβ has started in widespread cortical areas, the speed of continued Aβ aggregation is very constant and not affected to a large degree by different risk or protective factors.

    Now it is shown that, also, changes in CSF P-tau217 concentrations over time are quite similar between individuals after a tipping point has been reached. These results have implications for our understanding of the disease, and might also indicate that certain types of interventions must be initiated very early in the disease to be clearly effective.

    View all comments by Oskar Hansson

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References

News Citations

  1. Amyloid—It’s Not Whether, but for How Long You’ve Had It
  2. Can a Single Amyloid PET Scan Predict Time to Symptom Onset?
  3. Different CSF Phospho-Taus Match Distinct Changes in Brain Pathology
  4. Move Over Aβ, CSF P-Tau Tells Us There’s Plaque in the Brain
  5. Plasma p-Tau217 Set to Transform Alzheimer’s Diagnostics

Paper Citations

  1. Jack CR, Wiste HJ, Lesnick TG, Weigand SD, Knopman DS, Vemuri P, Pankratz VS, Senjem ML, Gunter JL, Mielke MM, Lowe VJ, Boeve BF, Petersen RC. Brain β-amyloid load approaches a plateau. Neurology. 2013 Mar 5;80(10):890-6. PubMed.
  2. Villemagne VL, Burnham S, Bourgeat P, Brown B, Ellis KA, Salvado O, Szoeke C, Macaulay SL, Martins R, Maruff P, Ames D, Rowe CC, Masters CL, Australian Imaging Biomarkers and Lifestyle (AIBL) Research Group. Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study. Lancet Neurol. 2013 Apr;12(4):357-67. Epub 2013 Mar 8 PubMed.

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