Ketones And Cognition: Insights From A Reverse-Translation Approach In Alzheimer Disease Mice
Bibliographic record
Abstract
Abstract Lifestyle factors are estimated to account for 40% of the risk of developing dementia. Despite this, trials of lifestyle-based interventions, including ketogenic dietary interventions, have shown mixed results in delaying dementia and evidence of both responder- and non-responder participants. In order to understand and eventually optimize ketogenic interventions for dementia, we are using a “reverse-translation” approach in which ketogenic strategies that show promise in humans are modeled in transgenic mouse models of Alzheimer’s disease (AD) in order to more clearly understand their mechanisms of action. Behavioral, anatomical and transcriptomic analyses in AD mouse models confirmed that two distinct ketogenic interventions (dietary enrichment with medium chain triglycerides and a high fat/low carb diet) both improved hippocampal-dependent learning and memory and modulated hippocampal neuronal structure and gene expression. Unexpectedly, despite their similar effects on brain function, these two ketogenic interventions showed markedly different effects on circulating ketone levels, suggesting underlying mechanisms that are independent of ketones. Indeed, metabolic and RNA sequencing experiments identified striking, diet-specific effects on multiple peripheral pathophysiological features of AD, including on glucose homeostasis, liver structure-function and the gut microbiome. These findings have important implications for the design of ketogenic and combinatorial lifestyle-based strategies for dementia.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".