Nutritional metabolism and cerebral bioenergetics in Alzheimer's disease and related dementias
Bibliographic record
Abstract
Disturbances in the brain's capacity to meet its energy demand increase the risk of synaptic loss, neurodegeneration, and cognitive decline. Nutritional and metabolic interventions that target metabolic pathways combined with diagnostics to identify deficits in cerebral bioenergetics may therefore offer novel therapeutic potential for Alzheimer's disease (AD) prevention and management. Many diet-derived natural bioactive components can govern cellular energy metabolism but their effects on brain aging are not clear. This review examines how nutritional metabolism can regulate brain bioenergetics and mitigate AD risk. We focus on leading mechanisms of cerebral bioenergetic breakdown in the aging brain at the cellular level, as well as the putative causes and consequences of disturbed bioenergetics, particularly at the blood-brain barrier with implications for nutrient brain delivery and nutritional interventions. Novel therapeutic nutrition approaches including diet patterns are provided, integrating studies of the gut microbiome, neuroimaging, and other biomarkers to guide future personalized nutritional interventions.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".