Relationship Between the Hepatic Iron Concentration and Glycaemic Metabolism, Prediabetes, and Type 2 Diabetes:A Systematic Review
Bibliographic record
Abstract
Context: Emerging research has suggested a potential link between high iron levels, indicated by serum ferritin levels, and the development of Type 2 Diabetes (T2D). However, the role of hepatic iron concentration (HIC) on T2D development and progression is not well understood. Objectives: This study aims to systematically review the literature on HIC and/or the degree of hepatic iron overload (HIO) in individuals with pre-diabetes and/or diagnosed T2D, and to analyse associations between HIC and markers of glucose metabolism. Data sources: The databases MEDLINE, PubMed, Embase, CINAHL and Web of Knowledge were searched for studies published in English from 1999 to March 2024. This review followed the Preferred Items for Systematic Reviews and Meta-Analyses checklist. Data Extraction: Data were extracted following the established eligibility criteria. Study characteristics and biomarkers related to pre-diabetes, type 2 diabetes (T2D), and hepatic iron overload (HIO) were extracted. The risk of bias was analysed using the Newcastle Ottawa Scale. Data was stratified by the exposure and analysed in sub-groups according to the outcome. Data regarding the HIC values in controls, pre-diabetic individuals and T2D subjects and the association estimates between HIC or HIO and markers of glycaemic metabolism, pre-diabetes or T2D were extracted. Data Analysis: A total of 12 studies were identified, and data from 4110 subjects were analysed. HIO was not consistently observed in pre-diabetic/T2D populations; however, elevated HIC was frequently observed in pre-diabetic and T2D subjects, and associated with the disruption of certain glycaemic markers in some cases. Conclusion: The extent of iron overload, as indicated by hepatic iron load, varied among the pre-diabetic and T2D populations studied. Further research is needed to understand the distribution and regulation of iron in T2D pathology.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".