Efficacy of low carbohydrate and ketogenic diets in treating mood and anxiety disorders: systematic review and implications for clinical practice
Bibliographic record
Abstract
BACKGROUND: There is mounting interest in the potential efficacy of low carbohydrate and very low carbohydrate ketogenic diets in various neurological and psychiatric disorders. AIMS: To conduct a systematic review and narrative synthesis of low carbohydrate and ketogenic diets (LC/KD) in adults with mood and anxiety disorders. METHOD: MEDLINE, Embase, PsycINFO and Cochrane databases were systematically searched for articles from inception to 6 September 2022. Studies that included adults with any mood or anxiety disorder treated with a low carbohydrate or ketogenic intervention, reporting effects on mood or anxiety symptoms were eligible for inclusion. PROSPERO registration CRD42019116367. RESULTS: = 389; age range 19 to 75 years) were included in the final analysis. This included nine case reports, two cohort studies and one observational study. Data quality was variable, with no high-quality evidence identified. Efficacy, adverse effects and discontinuation rates were not systematically reported. There was some evidence for efficacy of ketogenic diets in those with bipolar disorder, schizoaffective disorder and possibly unipolar depression/anxiety. Relapse after discontinuation of the diet was reported in some individuals. CONCLUSIONS: Although there is no high-quality evidence of LC/KD efficacy in mood or anxiety disorders, several uncontrolled studies suggest possible beneficial effects. Robust studies are now needed to demonstrate efficacy, to identify clinical groups who may benefit and whether a ketogenic diet (beyond low carbohydrate) is required and to characterise adverse effects and the risk of relapse after diet discontinuation.
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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 | 0.027 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".