Genetic Factors Contributing to Body Weight in Anorexia Nervosa and Bulimia Nervosa
Bibliographic record
Abstract
Anorexia nervosa (AN) is an eating disorder (ED) with substantial morbidity and the highest mortality among psychiatric disorders. Low body mass index (BMI) is the sine qua non of AN, and behaviours associated with reaching it are the primary reason for AN's high morbidity and mortality. Low BMI is also the main criterion diagnostically separating AN from bulimia nervosa (BN). The aim of this dissertation was to determine the role of genes regulating weight and appetite in BMI in AN and BN. Study 1 utilized carefully selected DNA samples to explore the role of markers in the leptin, melanocortin, and neurotrophin system genes with known or putative function in AN, BN, and controls, as well as in lifetime BMIs in EDs. Study 2 investigated dopamine pathway genes and FTO in weight regulation in a large sample of AN cases. The results revealed that an MC4R variant linked to antipsychotic-induced weight gain was underrepresented in AN, and AGRP and NTRK2 genetic variants were linked to minimum BMI in AN and maximum BMI in BN, respectively. In Study 2, a significant association between FTO and BMI at recruitment was observed. To our knowledge, this is the first study to utilize two distinct but complementary genetic approaches in the study of weight in EDs. These genetic findings may serve as an important first step toward gaining a better understanding of weight regulation in AN and BN, as well as having the potential for developing more effective treatment options and providing a highly specific target for novel medications. Alongside this work, other ED genetic studies utilizing different clinical phenotypes were also carried out during my PhD, and the papers published are inserted as appendices for reasons of thematic unity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".