Familial Resemblance in Eating Behaviors in Men and Women from the Quebec Family Study
Bibliographic record
Abstract
OBJECTIVE: It is commonly recognized that genetic, environmental, behavioral, and social factors are involved in the development of obesity. The family environment may play a key role in shaping children's eating behaviors. The purpose of this study was to estimate the degree of familial resemblance in eating behavioral traits (cognitive dietary restraint, disinhibition, and susceptibility to hunger). RESEARCH METHODS AND PROCEDURES: Eating behavioral traits were assessed with the Three-Factor Eating Questionnaire in 282 men and 402 women (202 families) from the Quebec Family Study. Familial resemblance for each trait (adjusted for age, sex, and BMI) was investigated using a familial correlation model. RESULTS: The pattern of familial correlation showed significant spouse correlation for the three eating behavior phenotypes, as well as significant parent-offspring and sibling correlations for disinhibition and susceptibility to hunger. According to the most parsimonious model, generalized heritability estimates (including genetic and shared familial environmental effects) reached 6%, 18%, and 28% for cognitive dietary restraint, disinhibition, and susceptibility to hunger, respectively. DISCUSSION: These results suggest that there is a significant familial component to eating behavioral traits but that the additive genetic component appears to be small, with generalized heritability estimates ranging from 6% to 28%. Thus, non-familial environmental factors and gene-gene and gene-environmental interactions seem to be the major determinants of the eating/behavioral traits.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".