Genetic Variation in Putative Salt Taste Receptors and Salt Taste Perception in Humans
Bibliographic record
Abstract
The objective of this study was to determine whether single nucleotide polymorphisms (SNPs) in the SCNN1A (3), SCNN1B (12), SCNN1G (6), and TRPV1 (10) genes affect salt taste perception. Participants were men (n = 28) and women (n = 67) from the Toronto Nutrigenomics and Health study aged 21-31 years. Taste thresholds were determined using a 3-alternative forced-choice staircase model with solutions ranging from 9×10(-6) to 0.5 mol/L. Suprathreshold taste sensitivity to 0.01-1.0 mol/L salt solutions was assessed using general labeled magnitude scales. None of the SNPs in the SCNN1A and SCNN1G genes were significantly associated with either outcome. In the SCNN1B gene, 2 SNPs in intronic regions of the gene modified suprathreshold taste sensitivity (mean iAUC ± SE). Those homozygous for the A allele of the rs239345 (A>T) polymorphism and the T allele of the rs3785368 (C>T) polymorphism perceived salt solutions less intensely than carriers of the T or C alleles, respectively (rs239345: 70.82±12.16 vs. 96.95±3.75, P = 0.02; rs3785368: 57.43±19.85 vs. 95.57±3.66, P = 0.03) In the TRPV1 gene, the rs8065080 (C>T, Val585Ile) polymorphism modified suprathreshold taste sensitivity where carriers of the T allele were significantly more sensitive to salt solutions than the CC genotype (98.3±3.8 vs. 74.1±8.3, P = 0.008). Our findings show that variation in the TRPV1 and the SCNN1B genes may modify salt taste perception in humans.
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) | 0.000 | 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 | 0.001 | 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".