High or increasing serum NfL is predictive of impending multiple sclerosis relapses
Bibliographic record
Abstract
BACKGROUND: One-off serum levels of neurofilament light chain (sNfL) is an established predictor of emerging disease activity in multiple sclerosis (MS). However, the importance of longitudinal increases in sNfL is yet to be enumerated, an important consideration as this test is translated for serial monitoring. Glial Fibrillary Acidic Protein (sGFAP) is another biomarker of predictive interest. Our objective was to assess the association between longitudinal changes sNfL and prediction of future relapses, as well as a possible role for sGFAP. METHODS: Participants with active MS were prospectively monitored for one year as part of a clinical trial testing mesenchymal stem cells. Visits every three months or less included clinical assessments, MRI scans and serum draws. sNfL and sGFAP concentrations were quantified with Single Molecule Array immunoassay. We used Kaplan-Meier estimates and Anderson-Gill Cox regression models with and without adjustment for age, sex, disease subtype, disease duration and expanded disability status score (EDSS) to estimate the rate of relapse predicted by baseline and longitudinal changes in biomarker. RESULTS: 58 Canadian and Italian participants with MS were enrolled in this study. Higher baseline sNfL was future relapse (Log-rank p = 0.0068), MRI lesions (p=0.0096), composite-relapse associated worsening (p=0.01) and progression independent of relapse activity (p=0.0096). Conversely, baseline sGFAP was only weakly associated with MRI lesions (0.044). Cross-sectional analyses of baseline sNfL revealed that a two-fold difference in baseline sNfL, e.g. from 10 to 20 pg/mL, was associated with a 2.3-fold increased risk of relapse during follow-up (95% confidence interval 1.65-3.17). Longitudinally, a two-fold increase in sNfL level from the first measurement was associated with an additional 1.46 times increased risk of relapse (1.07-2.00). The impact of longitudinal increases in sNfL on the risk of relapse were most pronounced for patients with lower baseline values of sNfL (<10 pg/mL: HR = 1.54, 1.06-2.24). These associations remained significant after adjustment for potential confounders. CONCLUSION: We enumerate the risk of relapse associated with dynamic changes in sNfL. Both baseline and longitudinal change in sNfL may help identify patients who would benefit from early treatment optimisation. TRIAL REGISTRATIONS: Canada:NCT02239393, Italy:NCT01854957&EudraCT, 2011-001295-19 CLASSIFICATION OF EVIDENCE: This study provides class 1 evidence that high baseline and longitudinal increases in sNfL are predictive of impending relapses in patients with active MS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".