An Evolutionary Genetic Perspective of Eating Disorders
Bibliographic record
Abstract
Eating disorders (ED) including anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED) affect up to 5% of the population in Western countries. Risk factors for developing an ED include personality traits, family environment, gender, age, ethnicity, and culture. Despite being moderately to highly heritable with estimates ranging from 28 to 83%, no genetic risk factors have been conclusively identified. Our objective was to explore evolutionary theories of EDs to provide a new perspective on research into novel biological mechanisms and genetic causes of EDs. We developed a framework that explains the possible interactions between genetic risk and cultural influences in the development of ED. The framework includes three genetic predisposition categories (people with mainly AN restrictive gene variants, people with mainly BED variants, and people with gene variants predisposing to both diseases) and a binary variable of either the presence or absence of pressure to be thin. We propose novel theories to explain the overlapping characteristics of the subtypes of AN (binge/purge and restrictive), BN, and BED. For instance, mutations/structural gene variants in the same gene causing opposite effects or mutations in nearby genes resulting in partial disequilibrium for the genes causing AN (restrictive) and BED may explain the overlap of phenotypes seen in AN (binge/purge).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".