Inflammatory Markers in Mother’s Own Milk and Infant Stool of Very Low Birthweight Infants
Bibliographic record
Abstract
BACKGROUND: Mother's breastmilk is the gold standard for feeding preterm infants. Preterm delivery may be precipitated by inflammatory maternal states, but little is known about milk cytokine profiles and how they correlate with markers of infant gut inflammation (i.e., stool calprotectin) in this vulnerable population. RESEARCH AIM: To assess cytokines and inflammatory markers in milk from parents of very preterm infants over time as well as correlations between milk and infant's stool calprotectin. METHOD: This is a secondary analysis of milk samples collected during OptiMoM, a triple-blind randomized clinical trial of infants born < 1250 g (NCT02137473). Longitudinally collected samples were analyzed for cytokines, choline, and inflammatory markers (C-reactive protein [CRP], IFN-γ, IL-10, IL-1β, IL-1ra, IL-6, IL-8, TNF-α). Infant stools were collected for longitudinal calprotectin analysis. Generalized estimating equations quantified longitudinal profiles of milk markers and stool calprotectin, their associations, and the correlation between free choline and C-reactive protein over follow-up. RESULT: Participants included 92 parents and infants (median weeks of gestation 27.3, median birth weight 845 g, and prevalence of male infants 45%). In all, 212 milk samples and 94 corresponding stool calprotectin levels were collected 1-11 weeks postpartum. C-reactive protein was present in much higher concentrations than other markers, and was highest in Week 1 postpartum. It decreased over time. IL-8 and free choline also changed over time while other markers did not. There was no correlation between any milk markers and stool calprotectin. CONCLUSION: Milk from mothers of very preterm infants has detectable inflammatory markers, some of which change over time. Research is needed to determine if infant outcomes are associated with these markers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".