Genetically predicted iron status and cardiovascular function and structure: a Mendelian randomization study
Bibliographic record
Abstract
AIMS: Iron levels imbalances are linked to cardiovascular outcomes. We aimed to assess the association between genetically predicted lifelong higher iron levels and cardiovascular outcomes, employing a two-sample Mendelian randomization (MR) approach to account for confounding biases. METHODS AND RESULTS: We used a study involving 257 953 subjects across six cohort studies that identified genetic variants consistently associated with iron biomarkers, including ferritin, serum iron, total iron binding capacity (TIBC), and transferrin saturation (TSAT). The UK Biobank study was used to investigate the association between the same genetic variants and left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), left ventricular ejection fraction (LVEF), left ventricular mass (LVM), and left ventricular mass-to-end-diastolic volume ratio (LVMVR). Two-sample MR approach was used in our main analysis. Heterogeneity, pleiotropy, bidirectional MR, MR-Egger, weighted median, and weighted mode were explored in the sensitivity analysis. One standard deviation (SD) increase in genetically predicted serum iron levels was associated with lower LVEDV (beta (95%CI): -0.11, (-0.19, -0.03), P-value = 0.006) and lower LVESV (-0.11 (-0.19, -0.03), P-value = 0.007). Moreover, one SD increase in genetically predicted TSAT was associated with higher LVMVR (0.09, (0.03, 0.15), P-value = 0.005). Heterogeneity, pleiotropy, and bidirectional effects were not observed. The identified associations were explained by HFE, TMPRSS6, TF, and TFR2 genes. No other associations were identified between iron biomarkers and cardiovascular outcomes. CONCLUSION: Our study provides MR evidence that iron status may alter cardiovascular function and structure. HFE, TMPRSS6, TF and TFR2 genes play a crucial role in the identified associations.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".