Effect of Genetic Variation on Salt, Sweet, Fat and Bitter Taste
Bibliographic record
Abstract
Background: Taste is one of the primary determinants of food intake and taste function can be influenced by a number of factors including genetics. However, little is known about the relationship between genetic variation, taste function, food preference and intake. Objective: To examine the effect of variation in genes involved in the perception of salt, sweet, fat and bitter compounds on taste function, food preference and consumption. Methods: Subjects were drawn from the Toronto Nutrigenomics and Health Study, a population of healthy men (n=487) and women (n = 1058). Dietary intake was assessed using a 196-item food frequency questionnaire (FFQ) and food preference was assessed using a 63-item food preference checklist. Subsets of individuals were phenotyped to assess taste function in response to salt (n=95), sucrose (n=95), oleic acid (n=21) and naringin (n=685) stimuli. Subjects were genotyped for Single Nucleotide Polymorphisms (SNPs) in candidate genes. Results: Of the SNPs examined in putative salt taste receptor genes (SCNN1(A, B, D, G), TRPV1), the rs9939129 and rs239345 SNPs in the SCNN1B gene and rs8065080 in the TRPV1 gene were associated with salt taste. In the TAS1R2 gene, the rs12033832 was associated with sucrose taste and sugar intake. The rs1077242 SNP in the bitter taste receptor gene TAS2R19 was associated with naringin taste and both grapefruit intake and preference. In the putative fat taste receptor CD36 the rs1761667 and rs1984112 SNPs were associated with intake of total, polyunsaturated and monounsaturated fats as well as oleic acid taste. Conclusions: Our findings demonstrate that genetic variation is associated with differences in taste function, food preference and intake across a number of taste modalities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".