Prenatal exposure to cigarette smoke interacts with <i>OPRM1</i> to modulate dietary preference for fat
Bibliographic record
Abstract
BACKGROUND: Preference for fatty foods is a risk factor for obesity. It is a complex behaviour that involves the brain reward system and is regulated by genetic and environmental factors, such as the opioid receptor mu-1 gene (OPRM1) and prenatal exposure to maternal cigarette smoking (PEMCS). We examined whether OPRM1 and PEMCS interact in influencing fat intake and whether exposure-associated epigenetic modifications of OPRM1 may mediate this gene-environment interaction. METHODS: We studied adolescents from a French Canadian genetic founder population, half of whom were exposed prenatally to maternal cigarette smoking. Fat intake was assessed with a 24-hour food recall in the form of a structured interview conducted by a trained nutritionist. The OPRM1 variant rs2281617 was genotyped for the whole sample with the Illumina Human610-Quad and HumanOmniExpress BeadChips. Methylation of blood DNA was assessed at 21 CpGs across OPRM1 in a subset of the sample using the Illumina HumanMethylation450 BeadChip. RESULTS: We included 956 adolescents in our study. In the whole sample, OPRM1 (T carrier in rs2281617) was associated with lower fat intake (-1.6%, p = 0.017), and PEMCS was associated with higher fat intake (+1.6%, p = 0.005). OPRM1 and PEMCS interacted with each other (p = 0.003); the "protective" (fat intake-lowering) allele of OPRM1 was associated with lower fat intake in nonexposed (-3.2%, p < 0.001) but not in exposed individuals (+0.8%, p = 0.42). Further, PEMCS was associated with lower DNA methylation across multiple CpGs across OPRM1 in exposed versus nonexposed individuals (p = 0.031). LIMITATIONS: A limitation of our study was its cross-sectional design. CONCLUSION: Our study suggests that PEMCS may interact with OPRM1 in increasing fat preference. Silencing of the protective OPRM1 allele in exposed adolescents might be related to epigenetic modification of this gene.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".