Associations of Lactoferrin-Fortified Formula with Infant Growth and Gut Microbiota: A Real-World Observational Study
Bibliographic record
Abstract
Background/Objectives: Lactoferrin, a key bioactive component in human milk, may bridge functional gaps in infant formula; however, its long-term effects on growth and the gut microbiota in term infants remain underexplored, particularly in real-world settings. Methods: This real-world evidence (RWE) study assessed the impact of lactoferrin-fortified formula (LF) on infant growth, the gut microbiota, and feeding tolerance compared with control formula (CF) and exclusive breastfeeding (BF). After propensity score matching (PSM) for maternal education level and infant age, 111 matched Chinese infants (37 per group: LF, CF, and BF; age: 6–12 months) were analyzed. Growth was evaluated using WHO Z-scores (WAZ, LAZ, WLZ, and zBMI). The gut microbiota was profiled via 16S rRNA sequencing (n = 81). Feeding challenges were quantified using the Montreal Children’s Hospital Feeding Scale (MCH-FS). Results: The LF group exhibited significantly higher length-for-age Z-scores (LAZ) compared with both the BF and CF groups (p < 0.001), indicating superior linear growth. LF infants also showed reduced MCH-FS scores (18.0 vs. 36.2 in CF; p < 0.001), signifying fewer feeding difficulties. Gut microbiota analysis revealed enrichment of Bifidobacterium breve and butyrate-producing taxa (e.g., Faecalibacterium and Ruminococcaceae), higher alpha diversity, and metabolic divergence, involving enhanced lysine fermentation to acetate/butyrate in LF infants, suggesting a higher level of short-chain fatty acid (SCFA) production. Beta diversity analysis demonstrated that the LF microbiota clustered close to BF. Conclusions: Lactoferrin-fortified formula was associated with improved linear growth and feeding tolerance while shaping a healthy gut microbiota, showing similarities to breastfed infants’ microbiota. These findings support LF fortification as a strategy to improve functional outcomes in formula-fed infants.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".