Frontiers in neurology · 2025
Cross-platform analytical assessment of serum GFAP quantification in multiple sclerosis: SIMOA versus two automated immunoassays.
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Tortosa-Carreres Jordi, Cubas-Núñez Laura, Castillo-Villalba Jéssica, Forés-Toribio Lorena, Gasque-Rubio Raquel, Quintanilla-Bordas Carlos, Alcalà-Vicente Carmen, Carratalà-Boscà Sara, Vaño-Bellver Ana, Laiz-Marro Begoña, Pérez-Miralles Francisco Carlos, Casanova Bonaventura
Аннотация
INTRODUCTION: Serum glial fibrillary acidic protein (sGFAP) is a promising biomarker, but its quantification mainly relies on SIMOA, a technology not widely available in clinical practice.
OBJECTIVES: To evaluate the analytical performance of two high-throughput automated platforms-Alinity® i (Abbott) and Lumipulse® G1200 (Fujirebio)-for sGFAP quantification.
METHODS: A retrospective longitudinal study included 107 serum samples from 23 MS patients. sGFAP was measured with SIMOA SR-X®, Lumipulse® G1200, and Alinity® i. Data were log-transformed. Agreement was assessed using Pearson correlations, Passing-Bablok regression, Bland-Altman analysis, and Δlog correlations between visits. Longitudinal differences across platforms were tested with a linear mixed-effects model (platform as fixed effect, SIMOA as reference). Moreover, ΔSIMOA was modeled against ΔLumipulse and ΔAlinity, adjusting for ΔEDSS, phenotype, relapses and new MRI lesions.
RESULTS: Passing-Bablok regression yielded slopes of 0.85 (SIMOA-Lumipulse), 0.81 (SIMOA-Alinity), and 0.95 (Lumipulse-Alinity), with intercepts of -0.32, -0.35, and -0.05. Mean log-biases were -0.622, -0.733, and 0.109. Correlations between log-means and log-differences were r = 0.26 (p = 0.006), 0.44 (p < 0.0001), and 0.15 (p = 0.13). The mixed-effects model showed no significant Δlog differences relative to SIMOA (p > 0.1). When modeling ΔSIMOA, ΔLumipulse was a significant predictor (β = 0.51; p = 0.002), whereas ΔAlinity showed only a trend (β = 0.31; p = 0.051). No clinical covariates were significantly associated.
CONCLUSION: Automated platforms, particularly Lumipulse, showed strong concordance with SIMOA supporting the role in analytical monitoring.
OBJECTIVES: To evaluate the analytical performance of two high-throughput automated platforms-Alinity® i (Abbott) and Lumipulse® G1200 (Fujirebio)-for sGFAP quantification.
METHODS: A retrospective longitudinal study included 107 serum samples from 23 MS patients. sGFAP was measured with SIMOA SR-X®, Lumipulse® G1200, and Alinity® i. Data were log-transformed. Agreement was assessed using Pearson correlations, Passing-Bablok regression, Bland-Altman analysis, and Δlog correlations between visits. Longitudinal differences across platforms were tested with a linear mixed-effects model (platform as fixed effect, SIMOA as reference). Moreover, ΔSIMOA was modeled against ΔLumipulse and ΔAlinity, adjusting for ΔEDSS, phenotype, relapses and new MRI lesions.
RESULTS: Passing-Bablok regression yielded slopes of 0.85 (SIMOA-Lumipulse), 0.81 (SIMOA-Alinity), and 0.95 (Lumipulse-Alinity), with intercepts of -0.32, -0.35, and -0.05. Mean log-biases were -0.622, -0.733, and 0.109. Correlations between log-means and log-differences were r = 0.26 (p = 0.006), 0.44 (p < 0.0001), and 0.15 (p = 0.13). The mixed-effects model showed no significant Δlog differences relative to SIMOA (p > 0.1). When modeling ΔSIMOA, ΔLumipulse was a significant predictor (β = 0.51; p = 0.002), whereas ΔAlinity showed only a trend (β = 0.31; p = 0.051). No clinical covariates were significantly associated.
CONCLUSION: Automated platforms, particularly Lumipulse, showed strong concordance with SIMOA supporting the role in analytical monitoring.