Ketogenic therapy for schizophrenia: evidence, mechanisms, and clinical perspectives
Bibliographic record
Abstract
Introduction: Schizophrenia is a chronic psychiatric disorder marked by significant cognitive and functional impairments. Current antipsychotic treatments offer limited benefit for negative symptoms and cognitive dysfunction while often exacerbating metabolic comorbidities. Emerging evidence implicates impaired glucose metabolism and mitochondrial dysfunction in the pathophysiology of schizophrenia, suggesting a role for metabolic interventions. Methods: This article reviews and synthesizes clinical, preclinical, and mechanistic evidence supporting the use of ketogenic therapy-a high-fat, low-carbohydrate intervention that induces ketosis-as a potential adjunctive treatment in schizophrenia. Results: Preliminary clinical findings, including case reports and small trials, suggest that ketogenic therapy may improve positive and negative symptoms, cognitive performance, and metabolic outcomes in individuals with schizophrenia spectrum disorders. Preclinical studies using NMDA antagonist models demonstrate that ketogenic interventions can normalize behavioral and neurophysiological deficits. Mechanistically, ketone bodies enhance mitochondrial function, modulate neurotransmitter systems (GABA, glutamate, dopamine), and reduce inflammation and oxidative stress. These effects may address core dysfunctions in schizophrenia that are unresponsive to dopamine-targeting pharmacotherapies. Discussion: Ketogenic therapy holds potential for addressing unmet clinical needs in schizophrenia, including negative and cognitive symptoms, treatment-resistant cases, and antipsychotic-induced metabolic syndrome. It may also be explored as a preventive strategy in high-risk populations. However, larger controlled trials are needed to establish efficacy, safety, and feasibility in psychiatric settings. Conclusion: Ketogenic therapy offers a novel, mechanistically informed intervention that targets metabolic and neurochemical pathways implicated in schizophrenia. If validated, it could pave the way for more integrative and personalized treatment strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".