Exploring the relationship between ultra-processed food consumption and gut microbiota at school age in a Brazilian birth cohort
Bibliographic record
Abstract
Dietary patterns significantly impact health outcomes and gut microbiota composition. However, longitudinal studies associating ultra-processed food consumption with gut microbiota composition, especially among adolescents in low- and middle-income countries, are lacking. This study aimed to explore this association using data collected from 364 participants at ages 6, 11, and 12 years from the 2004 Pelotas (Brazil) Birth Cohort. Microbiota data was obtained at age 12 after 16S rRNA gene sequencing of self-collected fecal samples. Linear or logistic regression models evaluated the relationship between age groups and gut microbiota outcomes (alpha diversity, beta diversity and relative abundances at the phylum and genus levels), considering dietary covariates and demographic, socioeconomic, health-related, and behavioral factors. No significant associations between ultra-processed food consumption and alpha diversity were observed after multiple testing corrections, and there was no strong evidence linking ultra-processed food consumption and beta diversity, with unweighted metrics explaining little variance at ages 11 and 12. Nominal associations were found between ultra-processed food and relative abundances of Actinobacteria (p = 0.032) and Proteobacteria (p = 0.045) (phyla), Bacteroides (p = 0.037 at age 6; p = 0.015 at age 11) and Peptostreptococcus (p = 0.025 at age 6; p = 0.010 at age 11) (genera). However, these associations lost statistical significance after adjustments for multiple comparisons. These findings highlight the need for more longitudinal studies to better understand the complex interaction between ultra-processed food intake and gut microbiota composition in adolescent populations in low- and middle-income countries.
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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 | 0.000 | 0.001 |
| 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".