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Record W4411755012 · doi:10.1093/brain/awaf234

Plasma periaxin is a biomarker of peripheral nerve demyelination

2025· article· en· W4411755012 on OpenAlexfundno aff
Roberto Bellanti, Ryan Keh, Stephen Keddie, Michael Chou, Mariya Misheva, Duncan Smyth, Georgios Baskozos, K Moodley, Melanie Hart, Alexander J. Davies, Mary M. Reilly, Simon Rinaldi, Michael P. Lunn

Bibliographic record

VenueBrain · 2025
Typearticle
Languageen
FieldMedicine
TopicPeripheral Neuropathies and Disorders
Canadian institutionsnot available
FundersGuarantors of BrainMedical Research Council CanadaGBS/CIDP Foundation International
KeywordsMedicinePolyradiculoneuropathyMultiple sclerosisPeripheral neuropathyBiomarkerPeripheralPeripheral nervous systemCohortDiseaseInternal medicineGuillain-Barre syndromeChronic inflammatory demyelinating polyneuropathyGastroenterologyImmunologyPathologyCentral nervous systemAntibodyEndocrinologyBiology

Abstract

fetched live from OpenAlex

Assessing disease progression and informing clinical trials in peripheral neuropathy would benefit from objective and responsive fluid biomarkers closely linked to disease biology. This is particularly important in chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) and Guillain-Barré syndrome (GBS), the most common inflammatory neuropathies, where reliable biomarkers of peripheral demyelination would help identify, and potentially measure, active disease and responses to treatment. We postulated that periaxin, a protein exclusively expressed by myelinating Schwann cells, could serve as a fluid biomarker of demyelinating peripheral neuropathy. We developed a single molecule array (Simoa)-based immunoassay to measure plasma periaxin in patients with CIDP (n = 45, including longitudinal samples across a discovery cohort and a validation cohort, for a total of 77 time points), GBS (n = 30, 66 time points), Charcot-Marie-Tooth disease (CMT, n = 20), CNS disease controls with multiple sclerosis (n = 30) and healthy controls (n = 30). We also evaluated whether periaxin is released in myelinating co-cultures following immune-mediated demyelination and axonal damage, comparing results with uninjured cultures. Plasma periaxin effectively distinguishes peripheral from CNS diseases, with significantly elevated levels in CIDP, GBS and CMT, but not in CNS disease or healthy controls (all P < 0.01). In CIDP, periaxin discriminates patients with active disease from those with inactive disease (P < 0.0001), and plasma levels decrease following treatment with intravenous immunoglobulin (IVIg). Elevated periaxin strongly predicts clinical worsening at 1 year [sensitivity 99%, specificity 72%, area under the curve (AUC) 0.86 (95% confidence interval, CI: 0.67-1)]. In GBS, peak levels of plasma periaxin and the ratio of periaxin to axonal biomarkers [neurofilament light chain (NfL) and peripherin] discriminate most cases of acute inflammatory demyelinating polyradiculoneuropathy (AIDP) from acute motor axonal neuropathy (AMAN), as classified by electrophysiology (sensitivity 100%, specificity 86%, AUC = 0.94, 95% CI: 0.81-1). Serial measurements showed that plasma periaxin levels peak 2 to 3 weeks after GBS symptom onset, followed by a gradual decline in the weeks thereafter. In vitro, periaxin is higher following immune-mediated demyelination compared with axonal damage and control conditions. Plasma periaxin is a biomarker of peripheral nerve demyelination. Combined with axonal fluid biomarkers and existing clinical scales, periaxin has the potential to improve the clinical management of peripheral neuropathies, accelerating advances in care and experimental research.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.285
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2025
Admission routes1
Has abstractyes

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