Bibliographic record
Abstract
Background: Breakfast timing is the most heritable across meals, and little is currently known about the contribution of genetic variation to food timing. As breakfast timing is not routinely assessed in epidemiological studies, alternative approaches may include analyses of phenotypically related dietary traits. Objective: We aimed to elucidate breakfast timing genetic variants through a genome-wide association study (GWAS) for breakfast cereals skipping, a more commonly assessed trait. Design: We leveraged the statistical power of the UK Biobank (n=193,860) to identify genetic variants related to breakfast cereals skipping as a proxy for breakfast skipping and applied an array of in silico approaches to investigate mechanistic functions and links to traits/diseases. Next, we attempted replication in other breakfast skipping GWAS in the TwinUK (n=2,006) and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium (n=11,963). Results: We identified six independent GWAS variants associated with breakfast skipping, including those implicated for caffeine (ARID3B), carbohydrate metabolism (FGF21), schizophrenia (ZNF804A), and those encoding enzymes important for N6-methyladenosine RNA transmethylation (METTL4, YWHAB, and YTHDF3), which regulates the pace of the circadian clock. Expression of identified genes was enriched in the cerebellum. Genome-wide correlation analyses indicated positive correlations with anthropometric traits. Through Mendelian randomization (MR), we observed causal links between genetically determined breakfast skipping and higher BMI, more depressive symptoms, and smoking. In bidirectional MR, we demonstrate a causal link between being an evening person and skipping breakfast, but not vice versa. We observed independent replication of our signals in a previous breakfast skipping GWAS in another British cohort (P=0.032), TwinUK, but not in a meta-analysis of non-British cohorts from the CHARGE consortium (P=0.095). Conclusions: Overall, our comprehensive GWAS approach identified six genetic variants for breakfast/food timing and supports the potential beneficial role of regular breakfast intake as part of a healthy lifestyle.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".