Prediction of future Alzheimer’s disease dementia using plasma phospho-tau combined with other accessible measures
Bibliographic record
Abstract
Abstract A combination of plasma phospho-tau (P-tau) and other accessible biomarkers may provide accurate prediction about the risk of developing Alzheimer’s disease (AD) dementia. We examined this in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) who were consecutively recruited in the BioFINDER study (n=340). The results were validated in SCD/MCI participants in the ADNI study (n=543). Plasma P-tau, plasma Aβ42/Aβ40, plasma neurofilament light, APOE genotype, brief cognitive tests and MRI (cortical thickness in AD-specific regions) were examined as predictors of progression to AD dementia primarily within 4 years. The accuracy was determined using the area under the ROC curve (AUC) from logistic regression models. Within 4 years, plasma P-tau217 predicted AD dementia accurately (AUC 0.83) in BioFINDER. A model of plasma P-tau217, memory, executive function, and APOE had higher accuracy (AUC 0.91, p<0.001). In ADNI, this model produced a similar AUC (0.90) using plasma P-tau181 instead of P-tau217. A cross-validated version of this model was implemented online for prediction of the individual probability of progressing to AD dementia. Within 2 and 6 years, parsimonious models performed similar in both cohorts (AUCs 0.90-0.91). Using cerebrospinal fluid P-tau, Aβ42/Aβ40 and neurofilament light instead of plasma biomarkers did not improve the accuracy. The clinical prediction by memory clinic physicians had significantly lower accuracy than all models (4-year AUC 0.71). In summary, plasma P-tau in combination with brief cognitive tests and APOE genotyping may greatly improve the diagnostic prediction of AD dementia and facilitate recruitment for AD trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".