Association of Hepatitis C Virus Infection with Serum Iron Status: Analysis of Data from the Third National Health and Nutrition Examination Survey
Bibliographic record
Abstract
BACKGROUND: There is growing evidence that mildly increased amounts of iron in the liver can increase hepatic injury, particularly if combined with other hepatotoxic factors, such as alcohol use, use of porphyrogenic drugs, or chronic viral hepatitis. In the present study, the association of hepatitis C virus (HCV) infection with serum measurements of iron status was assessed in the US population. METHODS: We analyzed data from a total of 14,462 participants in the Third National Health and Nutrition Examination Survey. We excluded subjects who were aged <12 years, subjects for whom measurements of serum levels of iron or ferritin or the results of liver function tests were missing, and subjects who had a serum transferrin saturation of > or =50% (to help exclude subjects with hemochromatosis). RESULTS: Mean serum levels of ferritin and iron (+/- standard error) were significantly higher among subjects with HCV infection (100+/-3 ng/mL and 229+/-17 microg/dL, respectively) than among subjects without liver disease (83+/-0.3 ng/mL and 101+/-2.1 microg/dL, respectively) (P<.0001). Serum levels of ferritin were directly and significantly correlated with serum levels of alanine aminotransferase, aspartate aminotransferase, and gamma-glutamyl transpeptidase (r=0.25, r=0.24, and r=0.28, respectively; P<.0001), whereas platelet counts were inversely correlated with serum levels of ferritin (r=-0.12; P<.0001). CONCLUSION: HCV infection is significantly associated with higher serum levels of ferritin and iron in the US population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".