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 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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".