Biological variation of Alzheimer’s disease plasma biomarkers in healthy individuals
Bibliographic record
Abstract
Abstract Background Plasma biomarkers for Alzheimer’s disease (AD) have demonstrated great performance to identify AD pathology in research cohorts, and recent studies also support their potential to monitor effects of disease‐modifying treatments. To properly interpret these biomarkers in research settings and to inform power calculations of future trials using them as surrogate outcomes, information on their physiological variability over time is needed. We conducted a 10‐week biological variation (BV) study to assess the within‐individual (CV I ; CV: coefficient of variation), between‐individual (CV G ) and analytical variability (CV A ) of blood‐based AD biomarkers. These parameters can provide the reference change value (RCV), which determines how much biomarker values must change to represent a significant abnormality‐related change, i.e . exceeding analytical and biological variation. Method Plasma samples were collected weekly for 10 weeks from 20 healthy individuals from the European Biological Variation Study (n=20; 50% female, median age 46.4). Biomarkers for tau pathology (p‐tau181), brain amyloidosis (Aβ42, Aβ40, Aβ42/40), glial activation (GFAP) and neurodegeneration (NfL) were quantified in duplicate using the Simoa technology. The CV‐ANOVA statistical method was used to compute BV estimates. To evaluate diurnal rhythm of these biomarkers, we also conducted a study with non‐demented older adults (n=24; 58‐82yo), with hourly blood collections during 26h in strictly‐controlled environmental conditions (NCT02091427). P‐tau217 (Lily) analyses are ongoing for both studies. Result Individual‐level (Figure 1) and pooled biomarker levels are shown (Figure 2), as well as BV estimates (Table 1). The 10‐week within‐individual physiological variation (CV I ) was lower for Aβ42/40 (4.3%), followed by Aβ42 (6.0%), Aβ40 (6.4%), NfL (7.9%), GFAP (9.3%) and p‐tau181 (15.8%). Between‐individual variation (CV G ) was lower for Aβ42/40 (8.8%), followed by Aβ42 (13.3%), Aβ40 (17.0%), p‐tau181 (20.5%), NfL (23.0%) and GFAP (31.5%). Analytical variation was low for all biomarkers (CV A range: 2.5‐6.6%). The RCV was higher for p‐tau181 (45.6%), followed by GFAP (31.7%), NfL (27.6%), Aβ40 (19.0%), Aβ42 (18.8%) and Aβ42/40 (15.6%). Conclusion Blood‐based AD biomarkers demonstrate good week‐to‐week stability, but their CV G and RCV’s can be rather high, with implications both for use in clinical routine (misclassification risk) and for future trial designs (high numbers of participants needed). Results for the diurnal variation study will also be presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 teacher head, 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".