- Researchers have developed a blood-based model that estimates when Alzheimer’s symptoms are likely to begin within a margin of about 3 to 4 years.
- The findings suggest a single blood test could act as a biological ‘clock’ helping identify individuals who are likely to develop cognitive symptoms within a specific time frame.
- If validated in broader populations, this approach could improve the design of prevention trials and support earlier, more personalized planning for people at risk of Alzheimer’s disease.
By this estimation, health and long-term care costs for Alzheimer’s and other forms of dementia are projected to reach nearly $1 trillion in 2050.
Predicting the onset of Alzheimer’s disease could be critically important for both clinical trials and practice, by offering a significant window for intervention.
Current methods to help predict Alzheimer’s onset typically include brain imaging scans or spinal fluid tests. However, these tests can be expensive and difficult to access.
Blood tests may offer a more feasible option as a predictive model, but historically have been less accurate than other options.
Now, a study published in Nature Medicine suggests that measuring a single blood sample for patterns of specific proteins may help to anticipate future symptom onset.
The research team, led by experts at Washington University School of Medicine in St. Louis, focused on a protein called p-tau217.
This protein, found in the plasma component of blood, is already known to reflect the abnormal accumulation of amyloid and tau proteins in the brain, which are hallmarks of Alzheimer’s disease
The study’s findings show that p-tau217 levels rise in the bloodstream in a consistent pattern as Alzheimer’s pathology develops.
By measuring p-tau217 and integrating those results into a statistical model, the research team could estimate the likely age when cognitive symptoms would begin, with a margin of error of 3 to 4 years.
Amyloid and tau proteins build up predictably in the brain over time. The researchers say this consistent pattern resembles tree rings.
In the same way that it is possible to determine how old a tree is from its rings, the researchers can use plasma p-tau217 levels to act like a ‘clock’ to strongly predict when someone is going to develop Alzheimer’s symptoms.
Speaking to Medical News Today, Kellen Petersen, PhD, study author and an instructor in neurology at WashU Medicine, explained how the ‘biological clock’ model improve upon existing methods for predicting disease progression.
“Most existing approaches can tell you if someone has changes in the brain related to Alzheimer’s disease or whether they are at higher or lower risk for developing symptoms, but they do not provide a clear estimate of when symptoms are likely to appear,” Petersen told us.
“Our clock model can estimate when someone developed abnormal p‑tau217 levels, which can then be used to predict symptom onset. This approach also reveals patterns, such as that older people develop symptoms faster after p‑tau217 becomes abnormal.”
– Kellen Petersen, PhD
In both groups, plasma p-tau217 was measured and compared with longitudinal clinical assessments. This revealed a strong relationship between rising blood levels of the protein and later development of cognitive symptoms.
Petersen commented on his surprise at how much quicker older adults with abnormal p-tau217 levels developed symptoms, saying that “one of the most striking findings was how much faster older adults developed symptoms after p‑tau217 became abnormal.”
“For example, people who first had abnormal p-tau217 levels around age 60 didn’t develop Alzheimer’s symptoms for about 20 years, whereas those who first had abnormal p-tau217 levels around age 80 developed symptoms after only about 10 years,” he noted.
“This suggests that age‑ and disease-related changes in the brain can influence how quickly Alzheimer’s symptoms manifest,” the study author explained.
The study used the PrecivityAD2 blood test. This blood test is already available clinically, albeit currently intended for use in those with existing cognitive impairment, and not yet for broader predictive screening.
Emer MacSweeney, MBBS, MRCP, FRCR, consultant neuroradiologist and CEO at Re:Cognition Health, who was not involved in the study, highlighted the possible clinical significance of this blood biomarker:
“This goes beyond existing diagnostic tools that typically identify pathology or risk and begins to translate that pathology into a timeline for clinical onset. Importantly, this model used longitudinal data from more than 600 cognitively unimpaired older adults and showed that the estimated age at plasma p-tau217 positivity correlated well with when cognitive symptoms actually started in real life.”
The ability to estimate when Alzheimer’s symptoms will begin years before they emerge could have several significant benefits, such as facilitating faster clinical trials and making prevention trials more efficient.
“An accuracy of roughly 3 to 4 years is not precise enough to be useful to an individual, but it is meaningful at the group level,” Petersen explained to MNT.
“For example, in a clinical trial that lasts 3 to 5 years, our models could help identify cognitively unimpaired people who are more likely to develop symptoms during the trial, which improves the chances of detecting whether a treatment works,” he added.
“As these models are improved by adding additional biomarkers and cognitive assessment information, we hope to narrow that margin of error to a point where it could be useful to individuals,” noted Petersen.
Eventually, the test may enable earlier detection and provide a clearer timeline for symptom development, helping with future planning and risk reduction.
Additionally, as a blood test, it may offer a far less invasive and inexpensive option than current alternatives, potentially expanding access to predictive tools.
According to the study author, “in the near term, we see the main use being in research and clinical trials, where there is a need to identify cognitively unimpaired individuals likely to develop Alzheimer’s symptoms within a defined time frame.”
“However, as these models improve and are validated in broader populations, similar approaches could be incorporated into clinical care.”
– Kellen Petersen, PhD
While more work is necessary to validate these models, blood-based clocks may open the door to earlier interventions and precision medicine in Alzheimer’s prevention.
“Assuming that predictions are accurate, they could help individuals to tailor both medical and lifestyle strategies to reduce the impact of Alzheimer’s,” Petersen added.
MacSweeney commented on the potential that earlier prediction could have for treatment decisions and lifestyle interventions, saying that “earlier prediction could reframe Alzheimer’s care from reactive to proactive.”
“In essence, accurate prediction gives both clinicians and patients a temporal roadmap rather than a simple yes/no risk status, enabling more personalised and potentially effective care strategies,” explained MacSweeney.
The study authors have made their modelling code publicly available, allowing other researchers to refine and build upon their work. A web-based application is also available to explore the clock models in greater detail.
In addition to p-tau217, additional blood-based biomarkers are also associated with cognitive symptoms in Alzheimer’s disease. The research team note that incorporating additional biomarkers could further improve prediction accuracy.
As the field of blood-based biomarkers continues to evolve, such tools could become part of routine assessment for individuals at risk of Alzheimer’s, bringing clinical care closer to early and personalized interventions.
Commenting on the role this test could have in clinical practice, MacSweeney told MNT: “Wider clinical adoption will depend on further validation in more diverse cohorts, regulatory approval, and the availability of effective interventions that justify pre-symptomatic testing.”
“Ultimately, if accuracy improves and actionable interventions become standard, it could transition into routine practice for early prognostication and personalized care planningm” she concluded.
