Effects of Whole-Grain and Sugar Content in Infant Cereals on Gut Microbiota at Weaning: A Randomized Trial
Bibliographic record
Abstract
The introduction of complementary foods during infancy marks an important step in the development of the infant gut microbiome. Infant cereals are popular weaning foods but consistent evidence on their effect on the intestinal microbiota, especially when differing in nutritional quality, is lacking. Fecal samples from 4–7-month-old Spanish infants who consumed infant cereals differing in whole grain and sugar content as first weaning foods were analyzed on changes in microbial composition by massively parallel sequencing of the 16S ribosomal RNA gene at baseline and after 7 weeks of intervention. Samples were obtained from a previous trial conducted in Spain demonstrating whole-grain cereal acceptability. In total, samples of 18 infants consuming 0% whole grain cereals with 24 g sugar (0-WG) and 25 infants consuming 50% whole grain cereals with 12 g sugar (50-WG) were analyzed. Microbial composition changed significantly over time (p = 0.001), per intervention group (p = 0.029) and per infant (p = 0.001). Abundance of genus Veillonella increased in both groups while Enterococcus decreased. Within the 0-WG group, phylum Actinobacteria decreased along with genus Bifidobacterium. In the 50-WG, we observed an increase in Lachnoclostridium and Bacteroides. In addition, 50-WG decreased Proteobacteria and Escherichia to levels lower than 0-WG. Although weaning itself appeared to be responsible for most changes, the increased presence of anaerobic fermenters together with inhibition of pathogenic Escherichia may indicate a supporting effect of infant cereals with 50% whole grains and a reduced sugar content over infant cereals manufactured with refined hydrolyzed flours on the infant microbiota. In fact, using a novel methodology for the identification of microbial signatures, we found two groups of microbial taxa predictive of infants consuming enriched whole-grain infant cereals with a high predictive value of about 93%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".