Multiple Indicators of Undernutrition, Infection, and Inflammation in Lactating Women Are Associated with Maternal Iron Status and Infant Anthropometry in Panama: The MINDI Cohort
Bibliographic record
Abstract
Maternal infections, nutrient deficiencies, and inflammation (MINDI) co-exist in lactating indigenous women in Panama, but their impact on maternal iron status and infant growth is unknown. For this secondary analysis of cross-sectional data of lactating mothers from our MINDI cohort, we investigated associations of MINDI variables with maternal anemia, elevated serum transferrin receptor (sTfR), low serum iron, hepcidin, ferritin, and infant weight-for-age (WAZ), length-for-age (LAZ), and head-circumference-for-age (HCAZ) Z-scores in 99 mother-infant dyads. A bootstrapping resampling procedure preselected covariates for inclusion in multivariable regressions models from chronic maternal infections and nutritional status [folate, vitamins A, D, retinol-binding protein (RBP), insulin-growth factor-1 (IGF-1)] and inflammation [C-reactive protein (CRP), cytokines, platelet indices] indicators. Anemia was prevalent (53.5%) but underestimated due to widespread low plasma volume (<2.2 L, 79.9%) and was associated with indicators of malnutrition [lower IGF-1, body mass index (BMI), vitamin D, and intake of green/leafy vegetables], but not inflammation. Higher CRP was associated with lower serum iron, and higher hepcidin and ferritin, whereas maternal platelets were associated with lower HCAZ (β = −0.22), WAZ (β = −0.17), and LAZ (β = −0.17). Higher LAZ was also associated with maternal serum vitamin D (β = 0.23), whereas maternal iron supplementation lowered LAZ (β = −0.22). Assessment of iron status in this MINDI cohort is complex and supplementation strategies must consider consequences for both the mother and the infant.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".