Genetic Variation in Taste and Its Influence on Food Selection
Bibliographic record
Abstract
Abstract Taste perception plays a key role in determining individual food preferences and dietary habits. Individual differences in bitter, sweet, umami, sour, or salty taste perception may influence dietary habits, affecting nutritional status and nutrition-related chronic disease risk. In addition to these traditional taste modalities there is growing evidence that "fat taste" may represent a sixth modality. Several taste receptors have been identified within taste cell membranes on the surface of the tongue, and they include the T2R family of bitter taste receptors, the T1R receptors associated with sweet and umami taste perception, the ion channels PKD1L3 and PKD2L1 linked to sour taste, and the integral membrane protein CD36, which is a putative "fat taste" receptor. Additionally, epithelial sodium channels and a vanilloid receptor, TRPV1, may account for salty taste perception. Common polymorphisms in genes involved in taste perception may account for some of the interindividual differences in food preferences and dietary habits within and between populations. This variability could affect food choices and dietary habits, which may influence nutritional and health status and the risk of chronic disease. This review will summarize the present state of knowledge of the genetic variation in taste, and how such variation might influence food intake behaviors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".