Serum ferritin level as an early indicator of metabolic dysregulation in young obese adults — a cross-sectional study
Bibliographic record
Abstract
The aim of this study was to investigate the relationship between serum ferritin level and antioxidative status and metabolic dysregulation in young adult obese population. This cross-sectional study included 300 subjects of either sex, grouped as obese and non-obese subjects. The body mass index, total iron binding capacity, fasting blood glucose, superoxide dismutase activity, and levels of serum ferritin, iron, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride, glutathione, and vitamin C were estimated. Analysis showed a significant alteration in all the parameters in obese adults. The correlation of ferritin level and body mass index showed a positive correlation (r = -0.81, p < 0.001, respectively) with levels of fasting blood glucose, superoxide dismutase, total cholesterol, low-density lipoprotein cholesterol, and triglyceride in obese individuals, whereas an insignificant correlation with vitamin C and glutathione level was observed in obese individuals. The significant positive correlation of ferritin level with the metabolic parameters and some antioxidative parameters in obese individuals signifies the development of metabolic disorders. Therefore, estimation of serum ferritin level will be an important early indicator for the risk of developing metabolic disorders in young adults.
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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 | 0.000 |
| 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 | 0.000 |
| 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".