Hyperferritinemia Is a Predictor of Onset of Diabetes in Japanese Males Independently of Decreased Renal Function and Fatty Liver: A Fifteen-Year Follow-Up Study
Bibliographic record
Abstract
Background: Type 2 diabetes is an important health concern worldwide. The disease etiology may depend on multiple environmental and genetic factors that cause insulin resistance, including dysregulation of iron storage. The goal of this study was to examine the relationship of the serum ferritin concentration with onset of diabetes over a long period. Methods: Correlations of serum ferritin and metabolic markers with onset of diabetes mellitus were examined over 15 years in 150 males participating in a health screening program. Results: HOMA-beta showed a gradual significant decrease in the first 4 years in subjects with ferritin > 190 ng/mL (group H) compared to those with ferritin <= 190 ng/mL, but there was no difference in HOMA-R between these groups. A significant number of cases with onset of diabetes was observed over 15 years (hazard ratio (HR): 3.97), and obesity, fasting blood glucose level, hemoglobin A1c (HbA1c), HOMA-R, fasting immunoreactive insulin (IRI) and C-peptide immunoreactivity (CPR) were all significant in univariate comparison between non-diabetes and diabetes-onset groups. In multivariate analysis, ferritin in group H (HR: 3.25), fatty liver (HR: 3.38), estimated glomerular filtration rate (eGFR) < 70 mL/min/1.73 m 2 (HR: 3.48) and high-density lipoprotein (HDL) < 40 mg/dL (HR: 2.61) were significant predictive factors for onset of type 2 diabetes mellitus. Conclusions: These results suggest that the serum ferritin level is an important index for priority intervention in preventive medicine for reduction of onset of diabetes. J Clin Med Res. 2021;13(12):541-548 doi: https://doi.org/10.14740/jocmr4635
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".