HLA Associations of Intrathecal <scp>IgG</scp> Production against Specific Viruses in Multiple Sclerosis
Bibliographic record
Abstract
Objective Specific human leucocyte antigen (HLA) alleles are not only associated with higher risk to develop multiple sclerosis (MS) and other autoimmune diseases, but also with the severity of various viral and bacterial infections. Here, we analyzed the most specific biomarker for MS, that is, the polyspecific intrathecal IgG antibody production against measles, rubella, and varicella zoster virus (MRZ reaction), for possible HLA associations in MS. Methods We assessed MRZ reaction from 184 Swiss patients with MS and clinically isolated syndrome (CIS) and 89 Swiss non‐MS/non‐CIS control patients, and performed HLA sequence‐based typing, to check for associations of positive MRZ reaction with the most prevalent HLA alleles. We used a cohort of 176 Swedish MS/CIS patients to replicate significant findings. Results Whereas positive MRZ reaction showed a prevalence of 38.0% in MS/CIS patients, it was highly specific (97.7%) for MS/CIS. We identified HLA‐DRB1*15:01 and other tightly linked alleles of the HLA‐DR15 haplotype as the strongest HLA‐encoded risk factors for a positive MRZ reaction in Swiss MS/CIS (odds ratio [OR], 3.90, 95% confidence interval [CI] 2.05–7.46, padjusted = 0.0004) and replicated these findings in Swedish MS/CIS patients (OR 2.18, 95%‐CI 1.16–4.02, padjusted = 0.028). In addition, female MS/CIS patients had a significantly higher probability for a positive MRZ reaction than male patients in both cohorts combined (padjusted <0.005). Interpretation HLA‐DRB1*15:01, the strongest genetic risk factor for MS, and female sex, 1 of the most prominent demographic risk factors for developing MS, predispose in MS/CIS patients for a positive MRZ reaction, the most specific CSF biomarker for MS. ANN NEUROL 2024;95:1112–1126
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".