Genetic variation in the epithelial sodium channel (ENaC) and salt taste perception in humans
Bibliographic record
Abstract
Despite individual differences in salt taste sensitivity no studies have identified variations in genes that explain this variability. Our objective was to determine whether single nucleotide polymorphisms (SNPs) in the epithelial sodium channel (ENaC), a putative salt taste receptor with 3 subunits, modifies salt (NaCl) taste in men (n=28) and women (n=67) aged 20–35 yrs. Taste thresholds (TT) 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 (STT) to 5 solutions (0.01–1 mol/L) was assessed using a general Labeled Magnitude Scale. Genotypes were extracted from an Affymetrix 6.0 chip (5 SNPs in SCNN1A; 34 SNPs in SCNN1B; 13 SNPs in SCNN1G ). A general linear model was used to compute differences between genotypes. No SNPs in the SCNN1A or SCNN1G genes modified salt taste. In the SCNN1B gene the rs9939129 (C>T) SNP modified TT (mmol/L ± SE) where carriers of the T allele detected significantly lower NaCl concentrations than CC homozygotes (2.33±0.76 vs 3.85±0.37, p=0.02). The rs239345 (A>T) SNP in this gene modified STT (iAUC ± SE) were those homozygous for the A allele perceived salt solutions less intensely than carriers of the T allele (70.82±12.16 vs. 96.95±3.75, p=0.02). Our findings indicate that variation in the SCNN1B gene modifies salt taste perception in humans. Grant Funding Source : The Advanced Food and Materials Network
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 | 0.001 |
| 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) | 0.002 | 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".