Biological variation estimates of Alzheimer’s disease plasma biomarkers in healthy individuals
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».