Mild cognitive impairment: when nutrition helps brain energy rescue—a report from the EuGMS 2020 Congress
Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment (MCI) is characterized by a decline in cognition and mainly affects older individuals above the age of 60. The global incidence of MCI varies, but it is often underdiagnosed and untreated. There is a distinct lack of approved pharmacologic options to treat MCI. There is, however, evidence to support the efficacy of nutritional interventions, such as ketogenic supplements/diets, which offer ketones as an alternative energy source to brain cells. This article explores the effect of ketones on metabolic activity in the brain and the mechanisms by which ketogenic medium-chain triglycerides (kMCTs) induce ketosis in patients with MCI. KEY TAKEAWAYS: This article reviews the effect of ketogenic supplements/diets on brain metabolism, including evidence supporting the efficacy of ketones as an efficient fuel for the brain. It discusses the use of oral nutritional ketogenic supplements, with particular reference to the 6-month randomized controlled BENEFIC trial, which showed that consumption of a kMCT drink, BrainXpert Energy Complex, improved cognitive performance in individuals with MCI compared with placebo. CONCLUSION: While there is a need for more long-term studies, results from the BENEFIC trial revealed the benefits of a brain-specific ketogenic supplement, as a nutritional intervention, on cognitive performance in individuals with MCI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 0.003 |
| 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".