Plasma biomarkers rates of change across the preclinical stage of Alzheimer’s disease: a longitudinal study
Bibliographic record
Abstract
Abstract Background Whether plasma biomarkers steadily increase during the preclinical stage of Alzheimer’s disease (AD) is unknown. Herein, we aimed to determine the rate‐of‐change of plasma biomarkers throughout preclinical AD. This may be important to determine the optimal time window for treatment. Method We included baseline and follow‐up plasma biomarkers measurements (follow‐up: 3.37±0.40 years) of 240 cognitively unimpaired participants of the ALFA+ cohort (mean age: 60.75±4.85 years). Plasma Aβ40, Aβ42, GFAP and NfL were measured with the Simoa N4PE Advantage Kit, and plasma p‐tau231 with a Simoa‐validated in‐house assay. For each participant we calculated the difference between follow‐up and baseline levels, and corrected for sex, time between measurements, and age. We computed z‐scores from the corrected values, and we applied a bootstrapped regression approach to model the rate‐of‐change of each plasma biomarker as a function of baseline age or Aβ PET centiloids (CL). Moreover, we also computed the plasma biomarkers rate‐of‐change in three stages of preclinical AD: (I) CSF/PET Aβ‐negative group, (II) CSF Aβ‐positive/PET Aβ‐negative (CL < 30), and (III) CSF/PET Aβ‐positive. Significance was determined if the 95% confidence interval of the rate‐of‐change did not overlap with zero. Result Significant acceleration in the rate‐of‐change of plasma GFAP was observed with ageing (Fig. 1), becoming significant at 60 years. There was also a significant accelerated change of p‐tau231 from 55 to 68 years. Plasma NfL was the only biomarker whose rate‐of‐change accelerated with Aβ accumulation, and that rate became significant at 40CL (Fig. 2). Consistently, we observed a significant acceleration of plasma NfL when there was overt Aβ pathology (CSF/PET Aβ‐positive group; Fig. 3) The rest of plasma biomarker rates‐of‐change did not significantly increase, some were even deaccelerating (plasma Aβ42/40 and p‐tau231), as Aβ accumulated (Fig. 2 and 3). Conclusion Plasma biomarkers rate‐of‐change throughout the preclinical AD continuum differ. Plasma NfL rate‐of‐change accelerates in the later stage of preclinical AD, when overt Aβ pathology is present. The deceleration on the rate‐of‐change of plasma Aβ42/40 and p‐tau231 may explain why their early increase in the preclinical AD continuum tends to plateau in later stages.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".