Association of Fecal Markers of Environmental Enteric Dysfunction with Zinc and Iron Status among Children at First Two Years of Life in Bangladesh
Bibliographic record
Abstract
Environmental enteric dysfunction (EED) causes gut inflammation and increased intestinal permeability leading to deficiencies in micronutrients such as zinc and iron. Fecal markers such as myeloperoxidase (MPO), neopterin (NEO), and alpha-1-anti-trypsin (AAT) can predict EED. The aim of this study was to examine the association between fecal markers of EED with zinc and iron status among children at first 2 years of life. Malnutrition and Enteric Disease Study Bangladeshi birth cohort data were used to conduct this analysis. Multivariable analyses using generalized estimating equations were performed to test the association between individual fecal markers with zinc or iron status of the children. A total of 265 children were enrolled in the study (male:female = 1:1). Of the 627 stool samples collected (N = 222 children), 535, 511, and 577 were accompanied by zinc, ferritin, and soluble transferrin receptor values, respectively. Median (interquartile range [IQR]) values of AAT, MPO, and NEO were 0.33 (0.18–0.62) mg/g, 3,895.42 (1,563.76–8,432.82) ng/mL, and 890.81 (331.57–2,089.04) nmol/L, respectively. Overall, 60%, 71%, and 97% of samples were above the values considered normal in nontropical settings for AAT, MPO, and NEO, respectively. High AAT levels were significantly associated with low ferritin values after adjusting for age and gender (coefficient = −5.85; 95% confidence interval = −11.23 to −0.47; P value = 0.03). No such association was found between AAT and plasma zinc status. Myeloperoxidase and NEO were not associated with plasma zinc or iron status. The study results imply the importance of enteric protein loss in contributing to reduced ferritin levels at first 2 years of life.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".