Neuromedin β : a strong candidate gene linking eating behaviors and susceptibility to obesity
Bibliographic record
Abstract
Background: Obesity is frequently associated with eating disorders, and evidence indicates that both conditions are influenced by genetic factors. However, little is known about the genes influencing eating behaviors. \n \nObjective: The objective was to identify genes associated with eating behaviors. \n \nDesign: Three eating behaviors were assessed in 660 adults from the Québec Family Study with the use of the Three-Factor Eating Questionnaire. A genome-wide scan was conducted with a total of 471 genetic markers spanning the 22 autosomes to identify quantitative trait loci for eating behaviors. Body composition and macronutrient and energy intakes were also measured. \n \nResults: Four quantitative trait loci were identified for disinhibition and susceptibility to hunger. Of these, the best evidence of linkage was found between a locus on chromosome 15q24-q25 and disinhibition (P < 0.0058) and susceptibility to hunger (P < 0.0001). After fine-mapping, the peak linkage was found between markers D15S206 and D15S201 surrounding the neuromedin β (NMB) gene. A missense mutation (p.P73T) located within the NMB gene showed significant associations with eating behaviors and obesity phenotypes. The T73T homozygotes were 2 times as likely to exhibit high levels of disinhibition (odds ratio: 1.8; 95% CI: 1.07, 2.89; P = 0.03) and susceptibility to hunger (odds ratio: 1.9; 95% CI: 1.15, 3.06; P = 0.01) as were the P73 allele carriers. Six-year follow-up data showed that the amount of body fat gain over time in T73T subjects was >2 times that than in P73P homozygotes (3.6 compared with 1.5 kg; P < 0.05
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".