Single nucleotide polymorphisms in sweet, fat, umami, salt, bitter and sour taste receptor genes are associated with gustatory function and taste preferences in young adults
Bibliographic record
Abstract
Taste is a fundamental mechanism whereby compounds are detected orally, yet it is highly variable among individuals. The variability in taste that is attributable to genetics is not well-characterized despite its potential role in food selection, and therefore, eating habits that contribute to risk of overweight and obesity. In order to implicate measures of taste function and preference as potentially deterministic factors in adverse eating behaviors that lead to obesity, it must be shown that a relationship exists between genetic variation in taste receptor genes and psychophysical measures of taste in the absence high body mass index. The primary objective of this pilot study was to investigate the relationship between single nucleotide polymorphisms (SNPs) in taste receptor genes and 3 different psychophysical measures of taste in healthy young adults. Sweet, salt, umami, fat, sour, and bitter taste receptor gene SNPs were genotyped in 49 participants (ages 24.6 ± 0.6 years) who completed testing to determine oral detection threshold (DT), suprathreshold sensitivity (ST) and taste preference (PR). A simultaneous association test was conducted between each SNP and the 3 taste outcomes (DT, ST, and PR). Twelve SNPs were associated with at least one of the 3 taste outcomes. Associations were observed between SNPs in taste receptor genes and psychophysical measures of sweet, fat, umami, and salt taste. These results suggest that differences in interindividual psychophysical measures of tastes, namely DT, ST, and PR, may be partially attributed to genetic variation in taste receptor genes. Future studies are warranted to investigate if these findings have consequences for habitual dietary intake of foods that elicit these tastes.
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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".