Medium-chain triglycerides and ketogenic diet prevent alterations of the gut microbiome in transgenic Alzheimer’s disease mice
Bibliographic record
Abstract
The systemic mechanisms underlying the benefits of ketogenic interventions on cognition in Alzheimer's disease (AD) are understudied. Interventions involving a carbohydrate-free high-fat ketogenic diet (KD) or dietary supplementation with medium-chain triglycerides (MCT) both improve cognition in AD mouse models, yet with opposing effects on circulating ketones levels, peripheral insulin sensitivity and inflammation. Since the gut microbiome regulates systemic metabolism and inflammation and is altered by aging and disease, we investigated how it is affected in mice subjected to MCT and KD. At early stages of pathology, AD mice exhibited substantially reduced richness and distinct composition of gut microbiome species. Administration of MCT or KD for 1-month increased microbiome diversity, restoring the levels of more than 50% of the bacteria altered in AD mice and inducing novel alterations. Both diets increased levels of short-chain fatty acid-producing bacteria, such as Lachnospiraceae, which directly correlated with improved hippocampal dendritic spine density. Interestingly, longer term administration of KD increased the obesity-associated Firmicutes/Bacteroidota ratio and bodyweight in AD but not WT mice, suggesting that AD-associated metabolic defects should be considered when designing such intervention. We conclude that MCT and KD may influence AD central and peripheral defects in part via modulation of the gut microbiome.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".