Nutritional Considerations for Healthy Aging and Reduction in Age-Related Chronic Disease
Bibliographic record
Abstract
A projected doubling in the global population of people aged ≥60 y by the year 2050 has major health and economic implications, especially in developing regions. Burdens of unhealthy aging associated with chronic noncommunicable and other age-related diseases may be largely preventable with lifestyle modification, including diet. However, as adults age they become at risk of "nutritional frailty," which can compromise their ability to meet nutritional requirements at a time when specific nutrient needs may be high. This review highlights the role of nutrition science in promoting healthy aging and in improving the prognosis in cases of age-related diseases. It serves to identify key knowledge gaps and implementation challenges to support adequate nutrition for healthy aging, including applicability of metrics used in body-composition and diet adequacy for older adults and mechanisms to reduce nutritional frailty and to promote diet resilience. This review also discusses management recommendations for several leading chronic conditions common in aging populations, including cognitive decline and dementia, sarcopenia, and compromised immunity to infectious disease. The role of health systems in incorporating nutrition care routinely for those aged ≥60 y and living independently and current actions to address nutritional status before hospitalization and the development of disease are discussed.
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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) | 0.001 | 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 | 0.001 | 0.002 |
| 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".