Abstract / Summary
Objectives: Plasma p-tau217 clocks have been reported to predict when Alzheimer's symptoms will begin, from a single blood test. This would transform Alzheimer's research and care. We evaluated the prognostic value of these models. Design: Analysis of a published prognostic study using data from two prospective longitudinal cohorts. Setting: Two observational research cohorts: the Knight Alzheimer Disease Research Center (Knight ADRC), a single US centre, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), a multicentre study in the US and Canada. Participants: 345 ADNI participants with longitudinal plasma %p-tau217 used to develop the clocks; 200 ADNI participants who were cognitively unimpaired (Clinical Dementia Rating 0) at blood draw; and 83 participants from both cohorts who progressed to symptomatic Alzheimer's disease in the original report. Main Outcome Measures: Age at symptom onset; time from %p-tau217 positivity to onset; time to onset from blood draw. Results: The clock predictor, estimated age at %p-tau217 positivity, is age at blood draw minus estimated time since positivity, so age is shared by the predictor and the outcome, age at symptom onset. Separated from age, the clock's %p-tau217 component explained essentially no variance (adjusted R2 -0.02 to 0.02). Age alone explained 0.54 to 0.71, compared with 0.34 to 0.61 reported for the clock. Raw %p-tau217 with age outperformed the clock in every analysis. For time from positivity to onset, the reported shorter interval at older positivity ages was reproduced when the %p-tau217 component was replaced with a random number. In survival analysis, estimated age at positivity was associated with symptom onset age (hazard ratio 0.87, 95% confidence interval 0.81 to 0.95), but not when onset was measured from blood draw, removing the shared age component (1.02, 0.96 to 1.09). Conclusions: The clock claims are unsupported. The reported accuracy comes from age shared between predictor and outcome, not from %p-tau217. Because timing is estimated only in known progressors, the model does not attempt to identify if an individual will progress, so it cannot answer the key question of when they will progress. Predictions from these models are unsupported for clinical or consumer use.