Genetic determinants of food preferences: a systematic review of observational studies
Bibliographic record
Abstract
BACKGROUND: Over the last decade, the results of several studies have indicated that adults' food preferences, consumption, and dietary choices vary depending on their genotype characteristics. However, the results of studies related to genes and polymorphisms involved in this phenomenon are contradictory. This study is a systematic review designed to evaluate the genetic determinants of food preferences. METHODS: This study was conducted following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Searches were conducted to identify articles testing the impact of genotypes on food choices, preferences, and intake in healthy adults. The search included all relevant keywords, and studies published between 1/1/1994 and October 2022 were considered. We assessed the quality of included studies and evaluated the risk of bias using the Newcastle-Ottawa Scale (NOS) for observational studies. RESULTS: A total of 8,510 records were identified through our search method, and finally, 50 studies were included in this study. The majority of the studies evaluated the association of genetic variants with preferences for macronutrients, sweet, bitter, and fatty foods. The results of our study suggest a significant correlation between TAS2R38 variants (rs713598, rs1726866, rs10246939) and bitter and sweet taste preferences. Additionally, we found a considerable association between the T102C polymorphism of the 5-HT2A receptor gene and a higher intake of protein, and rs1761667 (CD36) was associated with fat preference. CONCLUSION: In conclusion, this study revealed a significant association between certain genetic variants and food preferences among adults.
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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".