Relationships of Serum Ferritin, Transferrin Saturation, and <i>HFE</i> Mutations and Self-Reported Diabetes in the Hemochromatosis and Iron Overload Screening (HEIRS) Study
Bibliographic record
Abstract
OBJECTIVE: We evaluated the associations of self-reported diabetes with serum ferritin concentration, transferrin saturation (TfSat), and HFE C282Y and H63D mutations in six racial/ethnic groups recruited at five field centers in the Hemochromatosis and Iron Overload Screening (HEIRS) study. RESEARCH DESIGN AND METHODS: Analyses were conducted on 97,470 participants. Participants who reported a previous diagnosis of diabetes and/or hemochromatosis or iron overload were compared with participants who did not report a previous diagnosis. RESULTS: The overall prevalence of diabetes was 13.8%; the highest prevalence was in Pacific Islanders (20.1%). Of all participants with diabetes, 2.0% reported that they also had hemochromatosis or iron overload. The mean serum ferritin concentration was significantly greater in women with diabetes in all racial/ethnic groups and in Native-American men with diabetes than in those without diabetes. The mean serum ferritin concentration was significantly lower in Asian men with diabetes than in those without diabetes. Mean TfSat was lower in participants with diabetes from all racial/ethnic groups except Native-American women than in those without diabetes. There was no significant association of diabetes with HFE genotype. The mean serum ferritin concentration was greater (P < 0.0001) in women with diabetes than in those without diabetes for HFE genotypes except C282Y/C282Y and C282Y/H63D. Log serum ferritin concentration was significantly associated with diabetes in a logistic regression analysis after adjusting for age, sex, racial/ethnic group, HFE genotype, and field center. CONCLUSIONS: Serum ferritin concentration is associated with diabetes, even at levels below those typically associated with hemochromatosis or iron overload.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".