A review of the associations between single nucleotide polymorphisms in taste receptors, eating behaviors, and health
Why this work is in the frame
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Bibliographic record
Abstract
Food preferences and dietary habits are heavily influenced by taste perception. There is growing interest in characterizing taste preferences based on genetic variation. Genetic differences in the ability to perceive key tastes may impact eating behavior and nutritional intake. Therefore, increased understanding of taste biology and genetics may lead to new personalized strategies, which may prevent or influence the trajectory of chronic disease risk. Recent advances show that single nucleotide polymorphisms (SNPs) in the CD36 fat taste receptor are linked to differences in fat perception, fat preference, and chronic-disease biomarkers. Genetic variation in the sweet taste receptor T1R2 has been shown to alter sweet taste preferences, eating behaviors, and risk of dental caries. Polymorphisms in the bitter taste receptor T2R38 have been shown to influence taste for brassica vegetables. Individuals that intensely taste the bitterness of brassica vegetables ("supertasters") may avoid vegetable consumption and compensate by increasing their consumption of sweet and fatty foods, which may increase risk for chronic disease. Emerging evidence also suggests that the role of genetics in taste perception may be more impactful in children due to the lack of cultural influence compared to adults. This review examines the current knowledge of SNPs in taste receptors associated with fat, sweet, bitter, umami, and salt taste modalities and their contributions to food preferences, and chronic disease. Overall, these SNPs demonstrate the potential to influence food preferences and consequently health.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it