Association of <b><i>GLUT2</i></b> and <b><i>TAS1R2</i></b> Genotypes with Risk for Dental Caries
Bibliographic record
Abstract
To determine whether common polymorphisms in the sweet taste receptor (TAS1R2) and glucose transporter (GLUT2) genes are associated with dental caries, 80 healthy Caucasian individuals aged 21-32 years were genotyped and grouped based on the TAS1R2 (Ile191Val) and GLUT2 (Thr110Ile) polymorphisms. Clinical and radiographic examinations were conducted by a single examiner who was blinded to the genotypes. To assess caries prevalence, three different caries scores were determined: DMFT (decayed, missing, and filled teeth), DMFT + X-ray and ICDAS (International Caries Detection and Assessment System). Associations between genotypes and caries prevalence were analyzed using Student's t test. Based on the genotypes for each of the GLUT2 and TAS1R2 genes, individuals were stratified into four groups for comparison of caries scores. A higher DMFT score (mean ± SE; 4.3 ± 0.4 vs. 6.1 ± 1.2, p = 0.04) was observed among carriers of the Ile allele for GLUT2 (risk group). Carriers of the Val allele for TAS1R2 (resistant group) demonstrated lower caries scores: DMFT (4.1 ± 0.5 vs. 5.8 ± 0.9, p = 0.05), DMFT + X-ray (4.9 ± 0.6 vs. 7.5 ± 0.9, p = 0.01), and ICDAS (19.5 ± 2.2 vs. 26.14 ± 2.82, p = 0.03). Based on genotype stratification, caries scores were significantly lower in the double resistant group as compared to the double risk groups: DMFT (9.1 ± 0.08 vs. 4.2 ± 0.01, p < 0.01), DMFT + X-ray (10.5 ± 0.07 vs. 5.2 ± 0.01, p < 0.01) and ICDAS (32.9 ± 0.2 vs. 19.9 ± 0.01, p = 0.01). In conclusion, GLUT2 and TAS1R2 genotypes individually and in combination are associated with caries risk. Considering the combination of risk/resistance genotypes might further our understanding of genetic predispositions to dental caries and improve the accuracy of caries prediction models.
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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 | 0.000 | 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) | 0.002 | 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".