Associations of longitudinal plasma p-tau181 and NfL with tau-PET, Aβ-PET and cognition
Bibliographic record
Abstract
OBJECTIVE: To explore if changes over time of plasma phosphorylated tau (p-tau)181 and neurofilament light chain (NfL) predict future tau and amyloid β (Aβ) PET load and cognitive performance, we studied a subsample of the Alzheimer's disease (AD) neuroimaging cohort with longitudinal blood peptide assessments. METHODS: Eight hundred and sixty-five AD Neuroimaging Initiative participants were included. Using established AD cut-points for the cerebrospinal fluid concentrations of Aβ42, total-tau and p-tau181, subjects were classified according to the National Institute on Aging-Alzheimer's Association research framework, grouping markers into those of Aβ deposition (A), tau pathology (T) and neurodegeneration (N). Analysis of variance was used to compare the plasma biomarker data between the ATN groups. The rate of change over time of p-tau181 and NfL was obtained from linear mixed effects models and compared between the ATN groups. Linear regression analysis was used to investigate the association of baseline plasma biomarker concentrations and rates of change with future PET tau and Aβ load and cognitive performance. RESULTS: P-tau181 and NfL plasma concentrations increased along the AD spectrum, but only NfL showed greater rates of change in AD patients versus controls. Cognitive performance was associated cross-sectionally with NfL in all subgroups, and with p-tau181 only in AD spectrum individuals. The baseline concentrations of both plasma markers predicted PET Aβ and tau load and cognitive performance. The rate of change of NfL predicted future PET tau and cognitive performance. CONCLUSIONS: P-tau and NfL behave differently within the same individuals over time and may therefore offer complementary diagnostic information. TRIAL REGISTRATION NUMBER: NCT02854033, NCT01231971.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".