Nutrition to Prevent or Treat Cognitive Impairment in Older Adults: A GRADE Recommendation
Bibliographic record
Abstract
Aging is associated with cognitive declines leading to mild cognitive impairments or Alzheimer disease. Nutrition appear to protect from aging. Some dietary factors could either increase or protect against cognitive declines. This article aimed to provide GRADE recommendations related to nutrition aspects able to prevent or to treat cognitive impairments. A comprehensive literature review was performed using Medline database. The GRADE approach was used to classify quality of the existing evidence (systematic review or meta-analysis).The GRADE process led us to formulate seven key nutritional recommendations to manage cognitive declines, but did not allow us to do it for protein, vitamin B or antioxidants. Thus, 1) adherence to a Mediterranean diet (GRADE 1B); 2) high-level of consumption of mono- or poly- unsaturated fatty acids combined to a low consumption of saturated fatty acids (GRADE 1B); 3) high consumption of fruits and vegetables (GRADE 1B); 4) higher vitamin D intake (GRADE 1C) than the recommended daily allowance. In addition, a ketogenic diet, a low consumption of whole-fat dairy products or a caloric restriction are promising nutritional habits although the evidence does not yet support widespread uptake (GRADE 2C). In conclusion, nutrition is an important modifiable factor to prevent or protect against cognitive decline. Nevertheless, more studies are required to determine specific guidelines such as duration and amounts of nutrients to help older adult to maintain a healthy cognitive life.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".