A 3-Week Ketogenic Diet Increases Global Cerebral Blood Flow and Brain-Derived Neurotrophic Factor
Bibliographic record
Abstract
CONTEXT: The beneficial effects of a ketogenic diet (KD) on neurodegenerative conditions such as mild cognitive impairment (MCI) and Alzheimer disease (AD) are increasingly acknowledged, with potential implications for the general population as well. OBJECTIVE: Thus, our study aimed to explore the effect of a KD on cerebral blood flow (CBF) and brain-derived neurotrophic factor (BDNF) in healthy individuals. We hypothesized that a KD would increase CBF and BDNF, thereby presenting itself as an approach to prevent cognitive decline. METHODS: In total, 11 cognitively healthy individuals with overweight participated in a randomized, crossover trial consisting of 2 different 3-week interventions: (i) a KD; and (ii) a standard diet (SDD). Each diet period concluded with a positron emission tomography (PET) study day, accompanied by a separate magnetic resonance imaging (MRI) scan. Blood samples were collected prior to the PET scan to measure β-hydroxybutyrate (β-OHB) and BDNF levels. CBF was assessed using a [15O]H2O PET scan co-registered with an MRI scan. RESULTS: A KD led to increased basal plasma β-OHB levels compared to the SDD (647 [418-724] vs 50 [50-60] μmol/L, P < .05), increased CBF by 22% (P = .02), and elevated BDNF levels by 47% (P = .04). Moreover, a correlation was observed between β-OHB levels and CBF measurements across the 2 diets (R2 = 0.54, P < .001). CONCLUSION: Implementing a KD improved CBF and raised BDNF levels in cognitively healthy individuals, indicating that a KD should be assessed for as a potential treatment for conditions associated with reduced CBF.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".