Ferritin and Iron Levels Inversely Associated With Lymphoma Risk: A Mendelian Randomization Study
Bibliographic record
Abstract
Background: Current knowledge on iron's role in lymphoma development is very limited, with studies yielding inconsistent findings. To address this gap, we conducted a rigorous two-sample mendelian randomization study, aiming to elucidate the potential associations between iron storage and the risk of developing lymphoma. Methods: This study leveraged extensive genetic data derived from a comprehensive genome-wide association study (GWAS) comprising 257,953 individuals. The primary objective was to pinpoint single-nucleotide polymorphisms (SNPs) that are significantly associated with iron storage. Subsequently, this genetic information was analyzed in conjunction with summary-level data pertaining to lymphoma cases and controls, sourced from the IEU open GWAS project, which included a sample size of 3,546 lymphoma cases and 487,257 controls. To evaluate the relationship between iron storage and lymphoma risk, an inverse variance-weighted method with random effects was employed, complemented by rigorous sensitivity analyses. Results: Genetic predisposition to high ferritin and serum iron status was causally associated with lower odds of lymphoma. Ferritin exhibited an odds ratio (OR) of 0.777 (95% confidence interval (CI): 0.628 - 0.961, P = 0.020), indicating 22.3% reduced odds of lymphoma associated with a one standard deviation increase in ferritin levels. Similarly, serum iron demonstrated an OR of 0.776 (95% CI: 0.609 - 0.989, P = 0.040), corresponding to 22.4% decreased odds of lymphoma for a one standard deviation increase in serum iron. Conclusions: This study suggests that individuals with genes linked to higher iron storage levels have a lower risk of developing lymphoma, but further research is necessary before making any clinical recommendations.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".