You are what you eat: preliminary evidence of associations between dietary habits and oral microbiota composition in early childhood
Bibliographic record
Abstract
Background: Oral microbiota composition and diversity differ between obese and non-obese individuals. However, the associations between lifestyle habits (implicated in the pathogenesis of obesity) and the oral microbiota remain uncertain, particularly among children. \n \n Objective: To explore the associations between oral microbiota diversity and lifestyle habits among 8-10 year-old children. \n \n Methods: Data stem from the baseline assessment of the QUALITY cohort, a prospective cohort study of 630 children aged 8-10 years at recruitment, with a parental history of obesity. Lifestyle habits assessed include: physical activity by 7-day accelerometry, self-reported screen time, and dietary intake by 3 non-consecutive 24h dietary recalls. Fitness was measured by VO2peak. 16S-rRNA based microbial profiling of oral plaque samples obtained from 80 participants (40 normal weight, 40 overweight/obese) were performed to determine the diversity of the oral microbiota. Measures of diversity include Observed OTUs, Chao1, Shannon and Simpson reciprocal indices. Pearson's correlations assessed associations between diversity indices and lifestyle habits. \n \n Results: Percent carbohydrate intake was positively correlated with all measures of diversity (Obs OTUs r=0.22, P=0.06; Chao1 r=0.23, P=0.042; Shannon r=0.19, P=0.096; Simpson reciprocal r=0.20, P=0.076). Conversely, while not reaching statistical significance, modest negative correlations between total dietary fat and saturated dietary fat consumption and measures of oral microbiota diversity were noted (r = -0.14 to -0.17 across all indices). Physical activity, fitness and screen time were not associated with oral microbiota diversity at 8-10 yr. \n \n Conclusions: These preliminary findings suggest that dietary intake in childhood is associated with the bacterial diversity of the oral cavity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".