The Role of Iron in Type 1 Diabetes Etiology: A Systematic Review of New Evidence on a Long-Standing Mystery
Bibliographic record
Abstract
BACKGROUND: The incidence of type 1 diabetes (T1D) is rising, which might be due to the influence of environmental factors. Biological and epidemiological evidence has shown that excess iron is associated with beta-cell damage and impaired insulin secretion. AIM: In this review, our aim was to assess the association between iron and the risk of T1D. METHODS: A systematic literature search was performed in PubMed and EMBASE in July 2016. Studies investigating the effect of iron status/intake on the risk of developing T1D later were included, and study quality was evaluated. The results have been summarized in narrative form. RESULTS: From a total of 931 studies screened, we included 4 observational studies evaluating iron intake from drinking water or food during early life and the risk of T1D. The quality of the studies was moderate to high assessed via the nine-star Newcastle Ottawa Scale. One out of the four studies included in this review found estimates of dietary iron intake to be associated with risk of T1D development, whereas three studies found no such relationship for estimates of iron in drinking water. CONCLUSIONS: The limited number of studies included found dietary iron, but not iron in drinking water, to be associated with risk of T1D. Further studies are needed to clarify the association between iron and risk of T1D, especially studies including measurements of body iron status.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".