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Record W4416222300 · doi:10.1093/brain/awaf433

GFAP and NfL as predictors of disease progression and relapse activity in fingolimod-treated multiple sclerosis

2025· article· en· W4416222300 on OpenAlexaff
Aleksandra Maleska Maceski, Pascal Benkert, Maximilian Einsiedler, Sabine Schädelin, Johanna Oechtering, Lester Melie‐García, Alessandro Cagol, Riccardo Galbusera, Edoardo Galli, Sebastian Finkener, Patrice H. Lalive, Marjolaine Uginet, Jannis Müller, Caroline Pot, Amandine Mathias, Renaud Du Pasquier, Robert Hoepner, Andrew Chan, Giulio Disanto, Chiara Zecca, Marcus D’Souza, Lars G. Hemkens, Özgür Yaldizli, Patrick Roth, Claudio Gobbi, David Brassat, Björn Tackenberg, Rosetta Pedotti, Catarina Raposo, Jorge R. Oksenberg, Ari Green, Heinz Wiendl, Klaus Berger, Marco Hermesdorf, Fredrik Piehl, David Conen, Ludwig Kappos, Michael Khalil, Cristina Granziera, Ahmed Abdelhak, David Leppert, Eline A.J. Willemse, Jens Kühle, Amar Zadic, Juan Gómez, Suvitha Subramaniam, Mauricio Rodríguez, Lilian Demuth, Annette Orleth

Bibliographic record

VenueBrain · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsPopulation Health Research Institute
FundersNovartis PharmaEMD SeronoMedDay PharmaceuticalsInnosuisse - Schweizerische Agentur für InnovationsförderungSchweizerische Multiple Sklerose GesellschaftDeutschen Multiple Sklerose GesellschaftShionogiWestfälische Wilhelms-Universität MünsterNational Research FoundationBayer HealthCareGenentechInternational Progressive MS AllianceVetenskapsrådetFresenius Medical Care North AmericaH. Lundbeck A/STG TherapeuticsNovocureMultiple Sclerosis SocietyEuropean Committee for Treatment and Research in Multiple SclerosisSanofi GenzymeUniversität BaselF. Hoffmann-La RocheSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungAlexion PharmaceuticalsTeva Pharmaceutical IndustriesEli Lilly and CompanyU.S. Department of DefenseGW PharmaceuticalsSanofiCelgeneServierGilead SciencesBayerPfizerBiogenGlaxoSmithKlineBristol-Myers SquibbBundesministerium für Bildung und ForschungMerck KGaANational Science Foundation
KeywordsMultiple sclerosisFingolimodProportional hazards modelLongitudinal studyGlial fibrillary acidic proteinCohortDiseaseBiomarker

Abstract

fetched live from OpenAlex

In multiple sclerosis (MS) patients under therapy, the increase of serum glial fibrillary acidic protein (sGFAP) concentrations is associated with the course of 'progression in absence of relapse' (PIRA). While serum neurofilament light chain (sNfL) reflects both response as well as insufficient or lack of efficiency of disease-modifying therapies (DMT), the longitudinal course of sGFAP levels as a drug response marker for future PIRA in relation to specific types of DMT is less clear. We aimed to compare the predictive capacity of sGFAP and sNfL for PIRA and relapse activity and the longitudinal course in people with MS (PwMS) treated with fingolimod, based on Z scores derived from normative values. Overall, 420 PwMS under fingolimod treatment with follow-up of 9.1 years (interquartile range: 7.0-11.0) from the Swiss MS Cohort, contributing 2935 longitudinal serum samples, were included. A reference data set for sGFAP established from 4297 healthy controls across three European and North American cohorts was used to calculate Z scores. The longitudinal course and the predictive capacity of biomarkers for time to PIRA and relapse were assessed by Cox proportional hazards and linear mixed-effects models. In controls, sGFAP concentrations were 13.6% higher in females than males and increased exponentially with age. Altogether, 31.0% of PwMS experienced ≥1 PIRA event. Elevated sGFAP Z scores (>0.75) were associated with increased risk of PIRA [hazard ratio (HR): 1.64; 95% confidence interval (CI): 1.16-2.32; P = 0.006], while this was not the case for sNfL. Conversely, elevated sNfL predicted relapses (HR: 1.58; 95% CI: 1.13-2.23; P = 0.008), while sGFAP did not. Both biomarkers decreased under treatment: sGFAP by 0.19 Z score units (ZSU)/10 years (95% CI: -0.27 to -0.11; P < 0.001) and sNfL by 0.16 ZSU/10 years (95% CI: -0.27 to -0.06; P = 0.002). Serum GFAP remained elevated in PwMS with future PIRA events (estimate: 0.29; 95% CI: 0.07-0.50; P = 0.009); no such association was found for sNfL. Serum GFAP and sNfL Z scores provide complementary predictive capacity for PIRA and relapse risk. The decrease of sGFAP under fingolimod is a feature not observed with other types of DMT and may hint to a specific anti-neurodegenerative effect of Sphingosine-1-phosphate-receptor modulators on astrocytes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.324
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2025
Admission routes1
Has abstractyes

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