Anti-α-glucose–based glycan IgM antibodies predict relapse activity in multiple sclerosis after the first neurological event
Bibliographic record
Abstract
BACKGROUND: There is no specific serum-based biomarker for the diagnosis or prognosis of relapsing-remitting multiple sclerosis (RRMS). OBJECTIVE: We investigated whether levels of IgM antibodies to Glc(alpha1,4)Glc(alpha) (GAGA4) or to a panel of four glucose-based glycans could differentiate MS from other neurological diseases (OND) or predict risk of early relapse following first presentation (FP) of RRMS. METHODS: Retrospective analysis of 440 sera samples of three cohorts: A) FP-RRMS (n = 44), OND (n = 44); B) FP-RRMS (n = 167), OND (n = 85); and C) FP (n = 100). Anti-GAGA4 IgM levels were measured by enzyme immunoassay in cohort-A and cohort-B. Cohort-C IgM antibodies to glucose-based glycan panel were measured by immunofluorescence. RESULTS: FP-RRMS had higher levels of anti-GAGA4 IgM than OND patients (cohort-A, P = 0.01; cohort-B, P = 0.0001). Sensitivity and specificity were 27% and 97% for cohort-A; and 26% and 90% for cohort-B, respectively. In cohort-C, 58 patients experienced early relapse (<24 months), 31 had late relapse (> or =24 months), and 11 did not experience second attack during follow-up. Kaplan-Meier curves demonstrated decrease in time to next relapse for patients positive for the antibody panel (P = 0.02, log rank). CONCLUSIONS: Serum anti-GAGA4 IgM discerns FP-RRMS patients from OND patients. Higher levels of serum anti-alpha-glucose IgM in FP patients predict imminent early relapse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".