Predictors of Anorexia Nervosa and Obsessive‐Compulsive Disorder Comorbidity and Order of Diagnosis in a Danish National Cohort
Bibliographic record
Abstract
OBJECTIVE: Anorexia nervosa (AN) and obsessive-compulsive disorder (OCD) are highly comorbid; however, limited research has examined etiological pathways specific to individuals with AN developing OCD or individuals with OCD developing AN. This exploratory study aimed to identify factors influencing AN-OCD comorbidity with a focus on the order of diagnosis. METHOD: Using Danish national registers, 6449 individuals with AN and 9352 individuals with OCD were examined to assess the risk of subsequent OCD and AN. Explored predictors included parental characteristics, birth characteristics, childhood adversity, autoimmune and autoinflammatory diseases, psychiatric disorders, and prescriptions. Hazard ratios (HR) were calculated using Cox regression. Parallel analyses were conducted for the risk of subsequent anxiety disorder to determine predictors unique to AN-OCD comorbidity. RESULTS: Among individuals with AN, high birth weight (HR = 3.06) was uniquely associated with increased risk of subsequent OCD. For individuals with OCD, a history of other eating disorders (HR = 7.47) was associated with elevated risk of developing AN, whereas anxiety disorders in first-degree (HR = 0.32) and female first-degree relatives (HR = 0.22) were uniquely protective against AN. DISCUSSION: These exploratory findings suggest that distinct pathways may be involved in the order of onset for AN-OCD comorbidity. Specifically, for individuals with AN who subsequently developed OCD, high birth weight appeared to increase risk, whereas for individuals with OCD who later developed AN, familial anxiety disorders seemed to play a protective role. Findings could inform early screening and prevention efforts for individuals with AN at high risk for OCD, and vice versa.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".