A biomarker and endophenotype for anorexia nervosa?
Bibliographic record
Abstract
Objective: Recent research has suggested that a type of atypical eye movement, called square wave jerks, together with anxiety, may distinguish individuals with anorexia nervosa from those without anorexia nervosa and may represent a biomarker and endophenotype for the illness. The aim of this study was to identify the presence of this proposed marker in individuals currently with anorexia nervosa relative to healthy controls, and to identify the state independence and heritability of this putative marker by exploring whether it also exists in individuals who are weight-restored from anorexia nervosa and first-degree relatives (i.e. sisters of people with anorexia nervosa). Methods: Data from 80 female participants (20/group: current anorexia nervosa, weight-restored from anorexia nervosa, sisters of people with anorexia nervosa and healthy controls) were analysed. Square wave jerk rate was acquired during a fixation task, and anxiety was measured with the State Trait Anxiety Inventory. Results: Current anorexia nervosa, weight-restored from anorexia nervosa and sisters of people with anorexia nervosa groups made significantly more square wave jerks than healthy controls, but did not differ from one another. Square wave jerk rate and anxiety were found to discriminate groups with exceptionally high accuracy (current anorexia nervosa vs healthy control = 92.5%; weight-restored from anorexia nervosa vs healthy control = 77.5%; sisters of people with anorexia nervosa vs healthy control = 77.5%; p < .001). Conclusion: The combination of square wave jerk rate and anxiety was found to be a promising two-element marker for anorexia nervosa, and has the potential to be used as a biomarker or endophenotype to identify people at risk of anorexia nervosa and inform future treatments.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".