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Record W4415294507 · doi:10.1038/s41398-025-03591-1

Providing alternative fuel for the brain in anorexia nervosa: a review of the literature on ketones and their effects on metabolism and the brain

2025· review· en· W4415294507 on OpenAlexaff
Nadia Micali, Maria Consolata Miletta, Christoffer Clemmensen, Edoardo Pappaianni, François Lazeyras, Bernard Cuenoud, Carmen Sandi

Bibliographic record

VenueTranslational Psychiatry · 2025
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersNovo Nordisk FondenNovo Nordisk
KeywordsAnorexiaKetone bodiesAnorexia nervosaKetogenic dietEnergy metabolismPathophysiology

Abstract

fetched live from OpenAlex

Recent genetic findings have highlighted the importance of metabolic factors in contributing to risk for anorexia nervosa. The treatment of anorexia nervosa, however, has largely not yet attempted to modulate metabolic pathways, given that knowledge and understanding of potential metabolic targets and treatment goals remain limited. In search of potential metabolic mechanisms at play in psychiatric disorders, we look at the brain and beyond, focusing on ketones, related molecules, and nutritional therapies, e.g. the ketogenic diet, highlighting their brain and behavioural effects. The aim of this review is therefore to summarize knowledge on the physiology of ketones and related molecules, as well as evidence on their role on energy balance, brain, and behaviour. We then review the role ketones might have on the pathophysiology of anorexia nervosa, and how the intake of exogenous ketones and ketone precursors might impact on brain and behaviour in anorexia nervosa. We conclude with an hypothesised model explaining the potential role of ketones in the pathophysiology of anorexia nervosa and propose that exogenous ketones and ketone precursors could be an alternative source of brain energy in this illness and deserve further investigation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.702
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.330
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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