Serum ferritin and incident cardiometabolic diseases in Scottish adults
Bibliographic record
Abstract
BACKGROUND: Iron stores, estimated as ferritin levels, and type 2 diabetes (T2D) have been associated previously, while findings regarding coronary heart disease (CHD) and cerebrovascular disease (CEVD) are still inconclusive. No study has focused on simultaneous evaluation of associations between iron stores and the above cardiometabolic diseases (CMD) in the same population. We aim to evaluate the association between serum ferritin and risk of T2D, CHD and CEVD in Scottish population over a wide range of ferritin levels. METHODS: Longitudinal study in 6,497 participants of the 1995 and 1998 Scottish health surveys, who were followed-up until 2011. Cox regression models were conducted adjusting for age, sex/menopausal status, fibrinogen, GGT levels, smoking, alcohol consumption, total cholesterol, HDL-cholesterol, blood pressure, and BMI. Ferritin was used as continuous (sex/menopausal status-specific Z score) and categorical variable (sex/menopausal status-specific quartiles, quintiles and sextiles). RESULTS: During follow-up, 4.9% of the participants developed T2D, 5.3% CHD, and 2.3% CEVD. By using ferritin quartiles, serum ferritin was positively associated with T2D, CHD and CEVD but only the association with T2D remained after adjustment for covariates [Quartile 4 v. 1: adjusted HR 95% CI 1.59 (1.10-2.34); P = 0.006]. When ferritin sextiles were used (6 v. 1), the ferritin-CEVD association became slightly stronger and significant [adjusted HR 95% CI 2.08 (1.09-3.94); P = 0.024]. CONCLUSIONS: Iron stores relate differently to each CMD. Serum ferritin levels were positively and independently associated with incident T2D, and with incident CEVD if higher cut-off points for high ferritin levels were considered.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".