Association between Active H. pylori Infection and Iron Deficiency Assessed by Serum Hepcidin Levels in School-Age Children
Bibliographic record
Abstract
Hepcidin regulates iron metabolism. Its synthesis increases in infection and decreases in iron deficiency. The aim of this study was to evaluate the relationship between H. pylori infection and iron deficiency by levels of hepcidin in children. A total of 350 school-age children participated in this cross-sectional study. Determinations of serum ferritin, hemoglobin, hepcidin, C-reactive protein, and α-1-acid-glycoprotein were done. Active H. pylori infection was performed with a 13C-urea breath test. In schoolchildren without H. pylori infection, hepcidin was lower in those with iron deficiency compared to children with normal iron status (5.5 ng/mL vs. 8.2 ng/mL, p = 0.017); while in schoolchildren with H. pylori infection the levels of hepcidin tended to be higher, regardless of the iron nutritional status. Using multivariate analysis, the association between H. pylori infection and iron deficiency was different by hepcidin levels. The association between H. pylori and iron deficiency was not significant for lower values of hepcidin (Odds Ratio = 0.17; 95% Confidence Interval [CI] 0.02–1.44), while the same association was significant for higher values of hepcidin (OR = 2.84; CI 95% 1.32–6.09). This joint effect is reflected in the adjusted probabilities for iron deficiency: Individuals with H. pylori infection and higher levels of hepcidin had a probability of 0.24 (CI 95% 0.14–0.34) for iron deficiency, and this probability was 0.24 (CI 95% 0.14–0.33) in children without H. pylori infection and lower levels of hepcidin. In children with H. pylori infection and iron deficiency, the hepcidin synthesis is upregulated. The stimulus to the synthesis of hepcidin due to H. pylori infection is greater than the iron deficiency stimulus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".