Variation in the <b><i>TAS1R2</i></b> Gene, Sweet Taste Perception and Intake of Sugars
Bibliographic record
Abstract
BACKGROUND/AIMS: To determine whether variation in the TAS1R2 gene affects sucrose taste perception and sugar intake. METHODS: Participants were men (n = 238) and women (n = 458) aged 20-29 years. A subset (n = 95) with body mass index (BMI) data available completed a sensory analysis study. A food frequency questionnaire assessed dietary intake, and eight polymorphisms were genotyped (rs12033832, rs12137730, rs35874116, rs3935570, rs4920564, rs4920566, rs7513755 and rs9701796). Sucrose taste thresholds were determined by staircase procedure (solutions: 9 × 10-6 to 0.5 mol/l). Suprathreshold sensitivity to 0.01-1.0 mol/l sucrose solutions was assessed using general Labeled Magnitude Scales. RESULTS: A significant genotype-BMI interaction was observed for rs12033832 (G>A) for suprathreshold sensitivity (p = 0.01) and sugar intake (p = 0.003). Among participants with a BMI ≥25, G allele carriers had lower sensitivity ratings (mean incremental area under the taste sensitivity curve ± SE; GG/GA 54.4 ± 4.1 vs. AA 178.5 ± 66.6; p = 0.006), higher thresholds (GG/GA 9.3 ± 1.1 vs. AA 4.4 ± 4.3 mmol/l; p = 0.004) and consumed more sugars (GG/GA 130 ± 4 vs. AA 94 ± 13 g/day; p = 0.009). G allele carriers with a BMI <25 had lower thresholds (GG/GA 8.6 ± 0.5 vs. AA 16.7 ± 5.7 mmol/l; p = 0.02) and consumed less sugars (GG/GA 122 ± 2 vs. AA 145 ± 8 g/day; p = 0.004). CONCLUSION: The rs12033832 single nucleotide polymorphism in TAS1R2 is associated with sucrose taste and sugar intake, but the effect differs depending on BMI.
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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) | 0.003 | 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".