Interaction of <b><i>DRD2/ANKK1 Taq1A</i></b> Genotype with in-Store Retail Food Environment Exposures on Diet Quality in a Cohort of Quebec Adults
Bibliographic record
Abstract
BACKGROUND/AIMS: Gene-environment interactions may be relevant for nutrition outcomes. This study assessed the interaction between DRD2/ANKK1 Taq1A genotype and exposures to in-store retail food environment on diet quality. METHODS: CARTaGENE biobank data (n = 3,532) were linked to provincial food retail data. The Canadian adaptation of the Healthy Eating Index 2010 (HEI-C) was calculated from food frequency questionnaires. Generalized linear models adjusted for sociodemographic factors, anthropometrics, and energy intake were used to assess interactions between the Taq1A variant and retail food measures. RESULTS: A significant inverse interaction was observed between Taq1A and ice cream store displays on HEI-C score (estimate: -15.46 [95% confidence interval (CI): -24.83, -6.10], p = 0.0012) where, among allele carriers, increasing exposure to ice cream displays was associated with a lower HEI-C score as compared to allele carriers with a lower exposure. A significant positive interaction between Taq1A and price of vegetables was also observed, where, among allele carriers, increasing exposure to a higher price was associated with a higher HEI-C score compared to allele carriers with exposure to a lower price (estimate: 2.46 [95% CI: 0.78, 4.14], p = 0.0041). The opposite pattern was observed among non-carriers. CONCLUSIONS: DRD2/ANKK1 Taq1A is associated with adaptive responses to ice cream displays and vegetable prices, suggesting a differential susceptibility to retail environment food cues.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".