Induced Ketosis as a Treatment for Neuroprogressive Disorders: Food for Thought?
Bibliographic record
Abstract
Induced ketosis (or ketone body ingestion) can ameliorate several changes associated with neuroprogressive disorders, including schizophrenia, bipolar disorder, and major depressive disorder. Thus, the effects of glucose hypometabolism can be bypassed through the entry of beta-hydroxybutyrate, providing an alternative source of energy to glucose. The weight of evidence suggests that induced ketosis reduces levels of oxidative stress, mitochondrial dysfunction, and inflammation-core features of the above disorders. There are also data to suggest that induced ketosis may be able to target other molecules and signaling pathways whose levels and/or activity are also known to be abnormal in at least some patients suffering from these illnesses such as peroxisome proliferator-activated receptors, increased activity of the Kelch-like ECH-associated protein/nuclear factor erythroid 2-related factor 2, Sirtuin-1 nuclear factor-κB p65, and nicotinamide adenine dinucleotide (NAD). This review explains the mechanisms by which induced ketosis might reduce mitochondrial dysfunction, inflammation, and oxidative stress in neuropsychiatric disorders and ameliorate abnormal levels of molecules and signaling pathways that also appear to contribute to the pathophysiology of these illnesses. This review also examines safety data relating to induced ketosis over the long term and discusses the design of future studies.
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 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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".