Integrative analysis of taste genetics and the dental plaque microbiome in early childhood caries
Bibliographic record
Abstract
Early childhood caries (ECC) is a multifactorial disease mainly caused by the oral microbiome; however, it is also influenced by host genetics and environmental factors. This study investigates the interplay between genetic variants in taste-related genes and the microbiome in ECC. Using a case-control design involving 538 children, we obtained dental plaque microbiome profiles and genetic variants across 55 candidate genes. Including the socioeconomic factor index (SEFI) as one of the confounders, we find a significant association of three taste-gene variants with ECC and microbial diversity. However, there are no specific associations between the variants and cariogenic species. Furthermore, our analysis indicates that Streptococcus mutans is a partial mediator between these genetic variants and ECC outcomes. A combined machine learning model identifies Streptococcus mutans, rural-urban status, a bitter taste receptor variant, Candida dubliniensis, and SEFI as the top ECC-associated factors. Our findings underscore the need for multiomics approaches in ECC risk assessment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".