Habitual Iron Supplementation Associated with Elevated Risk of Chronic Kidney Disease in Individuals with Antihypertensive Medication
Bibliographic record
Abstract
The aim of this study was to examine the effects of habitual iron supplementation on the risk of CKD in individuals with different hypertensive statuses and antihypertension treatment statuses. We included a total of 427,939 participants in the UK Biobank study, who were free of CKD and with complete data on blood pressure at baseline. Cox proportional hazards regression models were used to examine the adjusted hazard ratios of habitual iron supplementation for CKD risk. After multivariable adjustment, habitual iron supplementation was found to be associated with a significantly higher risk of incident CKD in hypertensive participants (HR 1.12, 95% CI 1.02 to 1.22), particularly in those using antihypertensive medication (HR 1.21, 95% CI 1.08 to 1.35). In contrast, there was no significant association either in normotensive participants (HR 1.06, 95% CI 0.94 to 1.20) or in hypertensive participants without antihypertensive medication (HR 1.02, 95% CI 0.90 to 1.17). Consistently, significant multiplicative and additive interactions were observed between habitual iron supplementation and antihypertensive medication on the risk of incident CKD (p all interaction < 0.05). In conclusion, habitual iron supplementation was related to a higher risk of incident CKD among hypertensive patients, the association might be driven by the use of antihypertensive medication.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".