Biological variation estimates of Alzheimer’s disease plasma biomarkers in healthy individuals
Bibliographic record
Abstract
Abstract Introduction Blood biomarkers have proven useful in Alzheimer’s disease (AD), but little is known about their biological variation (BV), which plays a crucial role in the interpretation of individual patient data. Methods We measured plasma amyloid-β (Aβ42, Aβ40), phosphorylated tau (p-tau181, p-tau217, p-tau231), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) in fasting plasma samples collected weekly over 10 weeks from 20 participants aged 40-60y from the European Biological Variation Study. We determined within- (CV I ) and between-subject (CV G ) BV, analytical variation (CV A ) and reference change values (RCV). Results Biomarkers presented considerable variability in CV I and CV G . Aβ42/Aβ40 had the lowest CV I (∼3%) and p-tau181 the highest (∼16%), while the others ranged from 6-10%. Most RCVs ranged from 20-30% (decrease) and 25-40% (increase). Interpretation We provide BV estimates for AD plasma biomarkers, which can potentially refine their clinical and research interpretation. RCVs might be useful for detecting significant changes between serial measurements when monitoring early disease progression or interventions. Highlights · Plasma Aβ42/Aβ40 presents the lowest between- and within-subject biological variation, but also changes the least in AD patients vs controls. · Plasma p-tau variants significantly vary in their within-subject biological variation, but their substantial fold-changes in AD likely limits the impact of their variability. · Plasma NfL and GFAP demonstrate high between-subject variation, the impact of which will depend on clinical context. · Reference change values can potentially be useful in monitoring early disease progression and the safety/efficacy of interventions on an individual-level. · Serial sampling revealed that unexpectedly high values in heathy invidivuals can be observed, which urges caution when interpreting AD plasma biomarkers based on a single test result. Research in Context Systematic Review We reviewed PubMed for articles and conference abstracts that evaluated the biological variation (BV) of novel Alzheimer’s disease (AD) blood biomarkers. Two previous studies had reported BV estimates for serum glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL). Thus, we aimed to provide the first robust BV estimates for plasma amyloid-β (Aβ) and phosphorylated tau (p-tau) biomarkers, and also for plasma GFAP and NfL in in the same population. Interpretation Plasma biomarkers of key pathological features of AD demonstrate heterogeneity in their within- and between-subject variation. Plasma Aβ42/Aβ40 generally shows lower variability but also changes very modestly in AD patients vs controls. While plasma p-tau variants demonstrate higher variability, its clinical impact is likely limited due to large fold-increases in AD. Plasma NfL and GFAP had the largest between-subject variability, which may impact upon their application in certain contexts. Most research on blood biomarkers so far has been done using either single measurements or repeated measurements over longer (e.g., yearly) time frames; the weekly serial sampling in our study revealed that unexpected outlier values may occur, urging caution in clinical and research interpretation. Future directions Future studies should evaluate the potential clinical impact of the application of BV knowledge upon clinical and research interpretation of AD plasma biomarkers, especially in disease monitoring and in the evaluation of safety and efficacy of novel therapeutic interventions.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".