Nutrition and the ageing brain: Moving towards clinical applications
Bibliographic record
Abstract
The global increases in life expectancy and population have resulted in a growing ageing population and with it a growing number of people living with age-related neurodegenerative conditions and dementia, shifting focus towards methods of prevention, with lifestyle approaches such as nutrition representing a promising avenue for further development. This overview summarises the main themes discussed during the 3rd Symposium on “Nutrition for the Ageing Brain: Moving Towards Clinical Applications” held in Madrid in August 2018, enlarged with the current state of knowledge on how nutrition influences healthy ageing and gives recommendations regarding how the critical field of nutrition and neurodegeneration research should move forward into the future. Specific nutrients are discussed as well as the impact of multi-nutrient and whole diet approaches, showing particular promise to combatting the growing burden of age-related cognitive decline. The emergence of new avenues for exploring the role of diet in healthy ageing, such as the impact of the gut microbiome and development of new techniques (imaging measures of brain metabolism, metabolomics, biomarkers) are enabling researchers to approach finding answers to these questions. But the translation of these findings into clinical and public health contexts remains an obstacle due to significant shortcomings in nutrition research or pressure on the scientific community to communicate recommendations to the general public in a convincing and accessible way. Some promising programs exist but further investigation to improve our understanding of the mechanisms by which nutrition can improve brain health across the human lifespan is still required.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".