Ketogenic Approaches for the Treatment of Alzheimer’s Disease
Bibliographic record
Abstract
Dementia represents one of the largest and most urgent public health problems across the globe. Modeling projections have estimated that delaying the onset of Alzheimer's disease (AD) by 6 months would reduce the prevalence by 5%, while a delay of 12 months would reduce the prevalence by 10%. One approach to achieving a delay in the onset of AD is to investigate lifestyle interventions that could be widely implemented with a favorable risk-benefit relationship and socioeconomic profile. Amongst such interventions, there is increasing evidence to support the use of ketogenic interventions in AD. Indeed, it is well known that cerebral glucose metabolism is impaired in AD, even at a preclinical stage, and a growing body of literature suggests that these findings may represent a primary pathogenic mechanism leading to neurodegeneration. Ketones are readily taken up by the brain and can serve as an alternative energy source for neurons and glia, hypothetically bypassing the glucose uptake deficit in AD. In this invited review we discuss the preclinical as well as clinical work aiming to increase ketones as a primary intervention in AD, including variations of the ketogenic diet, medium chain triglyceride supplementation, and newer, more experimental approaches.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".