A meta-analysis of gene (5-HTT) × environment interactions in eating pathology using secondary data analyses
Bibliographic record
Abstract
Background Gene × environment (G × E) interactions in eating pathology have been increasingly investigated, however studies have been limited by sample size due to the difficulty of obtaining genetic data. Objective To synthesize existing G × E research in the eating disorders (ED) field and provide a clear picture of the current state of knowledge with analyses of larger samples. Method Complete data from seven studies investigating community (n = 1750, 64.5% female) and clinical (n = 426, 100% female) populations, identified via systematic review, were included. Data were combined to perform five analyses: 5-HTTLPR × Traumatic Life Events (0–17 events) to predict ED status (n = 909), 5-HTTLPR × Sexual and Physical Abuse (n = 1097) to predict bulimic symptoms, 5-HTLPR × Depression to predict bulimic symptoms (n = 1256), and 5-HTTLPR × Impulsivity to predict disordered eating (n = 1149). Results The low function (s) allele of 5-HTTLPR interacted with number of traumatic life events (P < .01) and sexual and physical abuse (P < .05) to predict increased likelihood of an ED in females but not males (Fig. 1). No other G × E interactions were significant, possibly due to the medium to low compatibility between datasets (Fig. 1). Conclusion Early promising results suggest that increased knowledge of G × E interactions could be achieved if studies increased uniformity of measuring ED and environmental variables, allowing for continued collaboration to overcome the restrictions of obtaining genetic samples. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.063 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".