Polymorphisms of the oxytocin receptor gene and overeating: the intermediary role of endophenotypic risk factors
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Oxytocin (OXT) is an evolutionarily ancient neuropeptide with strong links to affiliative and prosocial behaviors, and the management of stress. Increases in OXT also tend to decrease food intake, especially of sweet carbohydrates. The social correlates of low OXT levels mesh with the social deficits and stress proneness identified in interpersonal models of overeating, as well as the increased appetite for highly palatable foods typically seen in chronic overeaters. The objectives of this study were to investigate links between polymorphisms of the oxytocin receptor (OXTR) gene and overeating, and to examine OXTR links with relevant endophenotypes of overeating related to reward and stress sensitivity, and to food preferences. SUBJECT/METHODS: The sample comprised 460 adults between the ages of 25 and 50 years recruited from the community, and representing a broad range of body weights. Overeating, reward and punishment sensitivity, and food preferences, were quantified as composite variables using well-validated questionnaires. In addition, seven single-nucleotide polymorphisms (rs237878, rs237885, rs2268493, rs2268494, rs2254298, rs53576, rs2268498) of the OXTR gene were genotyped. RESULTS: Analyses identified a four-marker haplotype that was significantly related to food preferences. Individual genotype analyses also found that at least one of the markers was related to each of the phenotypic variables. In addition, an empirically derived structural equation model linking genetic and phenotype variables produced a good fit to the data. CONCLUSIONS: The results of this preliminary study have demonstrated that OXTR variation is associated with overeating, and with endophenotypic traits such as sweet and fatty food preferences, and reward and punishment sensitivity. In general, the genetic findings also favor the view that overeating may be associated with relatively low basal OXT levels.
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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 | 0.000 |
| 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) | 0.001 | 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".