Genetic variation in TAS1R2 taste receptor and sweet taste perception and sugar intake
Bibliographic record
Abstract
Taste is one of the primary determinants of food intake and variation in genes affecting taste pathways may influence taste perception and food intake. The TAS1R2 protein is part of the putative TAS1R2/TAS1R3 sweet taste receptor and likely plays a primary role in detecting sugars. Our objective was to determine whether single nucleotide polymorphisms (SNPs) in the TAS1R2 gene affect sweet (sucrose) taste (n = 95) and intake of sugars (n=535). Sucrose taste thresholds were determined using a 3 alternative forced choice staircase model with solutions ranging from 9 ×10 −6 to 0.5 mol/L. Genotypes for 23 SNPs in the TAS1R2 gene were extracted from an Affymetrix 6.0 chip. Sucrose and total sugar intake was calculated using a 196 item semi‐quantitative food frequency questionnaire. A general linear model was used to compute differences between genotypes. The rs12075191 (A>G) SNP was associated with sucrose taste and intake. Carriers of the A allele had lower taste thresholds (7.00 ± 1.65 vs 9.27 ± 0.63 mmol/L, p=0.058), lower total sugar intake (111.2 ± 7.3 vs 125.9 ± 2.4 grams, p = 0.05) and lower sucrose intake(41.1 ± 3.4 vs 48.7 ± 1.1 grams, p=0.03) than TT homozygotes. These results show that the rs12075191 SNP in the TAS1R2 gene modifies both sweet taste perception and sugar intake. Grant Funding Source : The Advanced Food and Materials Network
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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 | 0.001 |
| 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) | 0.004 | 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".