Multiple Sclerosis Susceptibility Genes: Associations with Relapse Severity and Recovery
Bibliographic record
Abstract
OBJECTIVE: Patients with early multiple sclerosis (MS) have stereotyped attack severity and recovery. We sought to determine if polymorphisms in MS susceptibility genes are associated with these attack features or with the risk of a second attack. METHODS: 503 white subjects evaluated within a year of MS onset were included in the study. The severity of and recovery from the first two attacks were determined based on published definitions. Seventeen MS susceptibility genes were genotyped at the UCSF MS Genetics laboratory. Each polymorphism was evaluated in multivariate ordinal models, adjusted for the other polymorphisms, for its association with attack severity and recovery. We also assessed if these polymorphisms were associated with increased risk of a second attack. RESULTS: The MPHOSPH9 polymorphism was associated with greater attack severity (odds ratios [OR] = 1.47, 95% CI [1.11, 1.94], p = 0.008), while the RGS1 and TNFRSF1A polymorphisms tended to be associated with reduced attack severity. The CD6 polymorphism tended to be associated with increased odds of worse attack recovery (OR = 1.25, 95% CI [0.93, 1.68], p = 0.13). In those who were HLA-DRB1-negative, the EVI5 polymorphism was associated with attacks of less severity; in HLA-DRB1 positive patients, EVI5 was associated with attacks of greater severity and worse recovery. The IL7R, TNFRSF1A, and GPC5 polymorphisms tended to be associated with having a second event within a year. CONCLUSIONS: Some MS susceptibility polymorphisms may be associated with attack severity, recovery, or frequency. Further characterization of these genes may lead to a better understanding of MS pathogenesis and to a more individualized treatment approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".