Integrative analysis of taste genetics and the dental plaque microbiome in early childhood caries
Bibliographic record
Abstract
Summary 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. The combinatorial analysis of these multiple factors influencing ECC susceptibility requires further research. This study investigated the interplay between genetic variants in taste-related genes and the microbiome in ECC, targeting taste genes because of their role in taste preference and potential interactions with oral fungi and bacteria. Using a case-control design involving 538 children, we obtained dental plaque microbiome profiles and genetic variants across 55 candidate genes through next-generation sequencing. Our association analysis for taste genetics and ECC outcome used the socioeconomic factor index (SEFI) and rural-urban status as confounders. We observed a few taste gene variants associated with ECC and microbial diversity. However, no specific association was observed between the variants and cariogenic species. Furthermore, our analysis indicated that Streptococcus mutans is a partial mediator between these genetic variants and ECC outcomes. Machine learning models integrating microbiome, genetics, and covariates achieved robust ECC vs. caries-free classification (AUROC = 0.96), with Streptococcus mutans, rural-urban status, a bitter taste receptor variant, Candida dubliniensis, and SEFI as the top ECC-associated factors. Our findings highlight the association between host genetics and the oral microbiome, underscoring the need for multiomics approaches in ECC risk assessment. Highlights S. mutans , C. dubliniensis , and taste genetic variants are associated with ECC. Rural-urban status and SEFI score are among the top social markers for ECC prediction. Taste-related genetic factors modulate the composition of dental plaque microbiome.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".