Leveraging transdiagnostic genetic liability to psychiatric disorders to dissect clinical outcomes of anorexia nervosa
Bibliographic record
Abstract
Abstract Anorexia nervosa (AN) has extensive genetic correlations with other psychiatric disorders, and genetic risk for different psychiatric disorders was associated with distinct clinical courses in AN. Uncovering associations between transdiagnostic psychiatric genetic liability and AN outcomes can facilitate its personalized treatment. In this study, we investigated the associations between transdiagnostic psychiatric genetic liability and outcomes of AN. Genomic structural equation models were fitted to genome-wide association data for AN and psychiatric disorders with high genetic correlations with AN (obsessive-compulsive symptoms [OCS], major depressive disorder [MDD], schizophrenia, and anxiety disorders) to extract one shared and five trait-specific genetic components. Next, we calculated the polygenic risk scores (PRS) for these components, including PRS shared , PRS AN-specific , PRS OCS-specific , PRS MDD-specific , PRS SCZ-specific and PRS ANX-specific , which index the shared genetic liability to all five psychiatric traits, and genetic liability specific to AN, OCS, MDD, SCZ and ANX, respectively. We then tested associations between these PRSs and clinical outcomes reported between 1997 and 2018 among AN cases from the Anorexia Nervosa Genetics Initiative (ANGI), linked to Swedish National Registers. The clinical outcomes included cumulative disease burden (i.e., number of diagnoses, medication prescriptions, and inpatient days), risks of psychiatric comorbidities, and AN symptomatology. Among 4028 included AN cases, the mean (SD) birth year was 1985 (9), and 3947 (98.0%) were female. Within AN, +1 SD increase of PRS shared was associated with 9–39% excess risk of disease burden and psychiatric comorbidity, whereas the associations between PRS AN-specific and most clinical outcomes were statistically non-significant. +1 SD increase of PRS MDD-specific was associated with 3–29% increased risk of AN disease burden. Our findings show that shared psychiatric liability is associated with more adverse AN outcomes, whereas AN-specific liability is not a good indicator for its clinical course. This study provides a novel perspective on factors influencing heterogeneity in AN clinical course.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".