Put your money where your mouth is: preliminary evidence that oral microbiota diversity may shape later cardiometabolic health in children
Bibliographic record
Abstract
Background: Emerging evidence suggests a link between the intestinal microbiota and cardiometabolic outcomes in both children and adults. The oral microbiota is less studied, and the association between the oral microbiota and cardiometabolic health in childhood remains largely unknown. \n \n Objective: To explore the associations between oral microbiota diversity measured at 8-10 yr and cardiometabolic health in childhood and adolescence. \n \n Methods: Data stem from the QUALITY cohort, a prospective cohort study of 630 children aged 8-10 years at recruitment, with a parental history of obesity. 16S-rRNA based microbial profiling of oral plaque samples obtained at baseline from 80 participants (40 normal weight, 40 overweight/obese) were performed to determine diversity of the oral microbiota. Measures of diversity include Observed OTUs, Chao1, Shannon and Simpson reciprocal indices. Measures of cardiometabolic health were assessed at 8-10 yrs, 10-12 yrs and 15-17 yrs, and include: fasting blood glucose, glucose 2hr post oral glucose load, homeostasis model of assessment (HOMA-IR), lipid profile (LDL cholesterol, HDL cholesterol and triglycerides), as well as age-, sex-, height-adjusted systolic (zSBP) and diastolic (zDBP) blood pressure z-scores. Pearson's correlations were used to estimate associations between diversity indices and cardiometabolic outcomes. \n \n Results: LDL cholesterol at 8-10 yr was positively correlated with all indices of microbiota diversity (Obs OTUs r=0.23, P=0.046; Chao1 r=0.22, P=0.055; Shannon r=0.24, P=0.035; Simpson reciprocal r=0.22, P=0.049). While correlations remained positive for LDL measured at 10-12 yr and 15-17 yr, they did not reach statistical significance. Similarly, microbiota diversity was positively correlated with zSBP at 8-10 yrs (r=0.22–0.25) and 10-12 yrs (r=0.22–0.25), not reaching statistical significance at 15-17 yrs. Indices of diversity at 8-10 yr were negatively correlated with fasting glucose (r=–0.27 to –0.31) and glucose 2hr post load (r=–0.27 to –0.29) 7 years later (at 15-17 yrs). Microbiota diversity was not correlated with HOMA-IR. \n \n Conclusions: These preliminary data in a small sample of children followed over 7 years suggest that oral microbiota diversity in early childhood may influence cardiometabolic health in later adolescence.
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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 | 0.003 |
| 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) | 0.006 | 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".