Effects of a Novel High‐Quality Protein Infant Formula on Energetic Efficiency and Tolerance
Bibliographic record
Abstract
OBJECTIVES: Protein overfeeding in infants can have negative effects, such as diabetes and childhood obesity; key to reducing protein intake from formula is improving protein quality. The impact of a new infant formula [study formula (SF)] containing alpha-lactalbumin, lactoferrin, partially hydrolyzed whey, and whole milk on growth and tolerance compared to a commercial formula (CF) and a human milk reference arm was evaluated. METHODS: This randomized, double-blind trial included healthy, singleton, term infants, enrollment age ≤14 days. Primary outcome was mean daily weight gain. Secondary outcomes were anthropometrics, formula intake, serum amino acids, adverse events, gastrointestinal characteristics, and general disposition. RESULTS: Non-inferiority was demonstrated. There were no differences between the formula groups for z scores over time. Formula intake [-0.33 oz/kg/day, 95% confidence interval (CI): -0.66 to -0.01, P = 0.05] and mean protein intake (-0.13 g/kg/day, 95% CI: -0.26 to 0.00, P = 0.05) were lower in the SF infants, with higher serum essential amino acid concentrations (including tryptophan) compared to the CF infants. Energetic efficiency was 14.0% (95% CI: 8.3%, 19.7%), 13.0% (95% CI: 6.0%, 20.0%), and 18.1% (95% CI: 9.4%, 26.8%) higher for weight, length, and head circumference, respectively, in SF infants compared to the CF infants. SF infants had significantly fewer spit-ups and softer stool consistency than CF infants. CONCLUSIONS: The SF resulted in improved parent-reported gastrointestinal tolerance and more efficient growth with less daily formula and protein intake supporting that this novel formula may potentially reduce the metabolic burden of protein overfeeding associated with infant formula.
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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 | 0.000 | 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".