Genetic variation in <i>HIF1A</i> is associated with smoldering inflammation and disease progression in Multiple Sclerosis
Bibliographic record
Abstract
ABSTRACT Background Understanding the mechanisms underlying disease progression in Multiple Sclerosis (MS) is fundamental to pave the way to treatment advances. Smoldering demyelinating inflammation characterized by iron deposition is observed at the edges of chronic active lesions and represents a relevant substrate of disease progression in MS. However, the influence of genetic factors on these mechanisms is not known. Leveraging the importance of iron deposition in smoldering inflammation, we assessed whether variants in genes belonging to iron-related pathways affect disease progression in MS. Methods We investigated the association between Single Nucleotide Polymorphisms (SNPs) mapping to 334 genes in iron-related pathways and the risk of disease progression, studying 2,817 MS patients from Italy (n=755) and Sweden (n=2,062), and comparing relapsing-remitting (RR-MS) with secondary progressive (SP-MS) disease course. To better understand the link of the identified variant with smoldering inflammation, we applied a multilayered approach using independent cohorts from Italy, Sweden and the United Kingdom and encompassing gene expression, PRL analysis, neurofilament levels, post-mortem spinal cord pathology and pharmacogenomics. Results We found an association between a locus in the Hypoxia-Inducible Factor 1-alpha ( HIF1A ) gene and the odds of SP-MS transition in the Italian cohort (rs11621525; SP-MS OR 0.57, 95% CI 0.44-0.72; P=3.30×10 - 6 ), which was replicated in the Swedish dataset (rs1951795; OR 0.79, 95% CI 0.67-0.95; P=0.0079). Additional analyses showed that patients carrying the protective allele exhibited reduced HIF1A expression in the immune cells, lower PRL volume, lower plasma/cerebrospinal fluid neurofilament levels, and lower inflammation and acute axonal injury in the post-mortem spinal cord. Moreover, the variant influenced the response to dimethyl fumarate, an approved MS drug with effect on mechanisms shared with HIF1A pathway. Conclusion A novel locus in the HIF1A gene, a crucial hub for iron-binding capacity, inflammation, and hypoxia response, is associated with the risk of disease progression in MS. Converging lines of evidence support the role of this locus in smoldering inflammation, prompting future studies to explore the potential of HIF1A as a therapeutic target in progressive 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".