Biases and consistency of different assay methods for neurological biomarkers using Quanterix single-molecule-array technology: A comparative study of the secondary analysis method
Notice bibliographique
Résumé
Breakthrough technologies such as the Single molecule array (Simoa) technology developed by Quanterix provide higher sensitivity, enabling measurement of central nervous system-abundant proteins in blood. Neurofilament light chain (NfL) and glial fibrillary protein (GFAP) are two proteins that have attracted considerable attention as biomarkers for many neurological conditions. Due to their performance and utility, these biomarkers are available in different Quanterix assays, including single-plex and multi-plex assay setups. Limited research has been conducted to evaluate how modifications in assay formulations may impact overall analytical performance and comparability. We recently established reference intervals (RI) for plasma NfL and GFAP measured in normative samples from the Canadian Health Measures Survey (CHMS) using the Quanterix Neurology 4-Plex E (N4PE) Advantage. The aim of the present study was to perform method comparisons to assess how well CHMS RIs generated on the N4PE assay translate to other assay formulations to facilitate generalizability and uptake. Seven independent comparisons were conducted, each using a total of 80 plasma samples from the CHMS. These samples were divided (n=40 each) based on the two N4PE lots previously used to generate RIs. The following Quanterix assay formulations were evaluated against the N4PE anchor assay: NfL Advantage PLUS (NfL+), GFAP Advantage PLUS (GFAP+), Neurology 2-Plex B Advantage (N2PB), Neurology 2-Plex B Advantage PLUS (N2PB+), Neurology 4-Plex B Advantage (N4PB), Neurology 4-Plex D PLUS (N4PD+), and Neurology 4-Plex E PLUS (N4PE+). Plasma biomarker concentrations were measured using the Quanterix Simoa HD-X analyzer, with assays run as per the manufacturer's specifications. Assay comparisons were conducted using Spearman correlation and Bland-Altman analysis to assess bias between assays. Both NfL and GFAP concentrations were tightly correlated between assay formulations with rho > 0.9 and a P-value < 0.0001 for all assay crosses. However, the bias between formulations ranged from 0.5% to 42.1%, indicating that data correction may be required to harmonize data generated from different assay formulations. In summary, this study emphasizes the importance of data correction when using different assay methods to ensure data comparability across studies. Additionally, the study results support the applicability of RIs and suggest that when using RIs, corrections should be made based on the assay-specific bias. Future studies should further expand the sample range, particularly in high-concentration conditions such as acute neurological injury, to comprehensively evaluate the performance of different assay formulations.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».