# Cross-platform analytical assessment of serum GFAP quantification in multiple sclerosis: SIMOA versus two automated immunoassays

## TL;DR

The study compares two automated methods for measuring a potential biomarker in multiple sclerosis and finds they align well with a specialized method.

## Contribution

The study evaluates the analytical performance of two automated immunoassays for serum GFAP quantification in MS patients.

## Key findings

- Lumipulse and Alinity platforms showed strong agreement with SIMOA for sGFAP quantification.
- Longitudinal differences across platforms were not significant relative to SIMOA.
- ΔLumipulse was a significant predictor of ΔSIMOA changes in sGFAP levels.

## Abstract

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.

To evaluate the analytical performance of two high-throughput automated platforms—Alinity® i (Abbott) and Lumipulse® G1200 (Fujirebio)—for sGFAP quantification.

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.

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.

Automated platforms, particularly Lumipulse, showed strong concordance with SIMOA supporting the role in analytical monitoring.