The health impacts and genetic architecture of food liking in cardio-metabolic diseases
Bibliographic record
Abstract
We evaluated temporal and genetic relationships between 176 food-liking-traits and cardio-metabolic diseases using data from the UK Biobank (N = 182,087) for observational analyses and summary-level GWAS data from FinnGen and other consortia (N = 406,565-977,323) for genetic analyses. Integrating observational and genetic results, we identified two detrimental food-liking-traits (bacon and diet-fizzy-drinks) and three protective food-liking-traits (broccoli, pizza, and lentils/beans). These food-liking-traits are associated with habitual food intake and influence cardio-metabolic proteins and biological processes. Notably, we found three genetic links: diet-fizzy-drinks with heart-failure, bacon with type-2-diabetes, and lentils/beans with type-2-diabetes, identifying 54 pleiotropic single-nucleotide-variants, impacting both phenotypes. Our data show the diet-fizzy-drinks and heart-failure link maybe not direct, as diet-fizzy-drinks liking correlates with sweet food consumption and shares variants linked to BMI, adiposity, platelet count and cardio-metabolic traits. The pleiotropic single-nucleotide-variants map to 251 tissue-specific genes, with four showing high druggability potential, highlighting personalized dietary strategies for cardio-metabolic diseases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".