Animal-based ketogenic diet puts severe anorexia nervosa into multi-year remission: A case series
Bibliographic record
Abstract
Background: Anorexia nervosa is a devastating condition that increases risk of death over five-fold and is associated with a high rate of relapse. Considering the growing field of metabolic psychiatry, anorexia can be framed as a ‘metabolic-psychiatric’ condition that may benefit from treatment with metabolic health interventions with neuromodulatory properties. Ketogenic diets, very low carbohydrate high-fat diets, are one such neuromodulatory intervention with a long history of use in epilepsy and more recently in other systemic, neurological and mental health conditions.Aim: To describe clinical cases that highlight the potential of ketogenic diets in the treatment of anorexia and the need for further research.Setting: Patient interviews were conducted via telemedicine.Methods: Medical interviews and chart reviews were conducted with three patients with severe anorexia. Written informed consent was provided by all participants.Results: Patients with anorexia, body mass index (BMI) nadirs of 10.7 kg/m2, 13.0 kg/m2 and 11.8kg/m2 and refractory to standard of care therapy, each achieved remission of between 1–5 years to date on a high-fat animal-based ketogenic diet. Patients exhibited not only improvements in weight, with weight gain of over 20 kg each, but also diminution of anxiety and overall enhanced mental well-being.Conclusion: These cases suggest a ketogenic diet may be useful for some patients with anorexia. Further research is needed.Contribution: This case series is the first to document treatment of anorexia with unimodal ketogenic diet intervention and raises provocative questions about the role of this neuromodulatory dietary treatment for patients with anorexia, as well as the neurometabolic nature of the disease itself.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".