Engaging the complexity of diet and healthy aging in humans
Bibliographic record
Abstract
Abstract Little is known about how normal variation in dietary patterns in humans affects the aging process, largely because both nutrition and the physiology of aging are highly complex and multidimensional. Here, we apply the nutritional geometry framework to data from 1560 older adults followed over four years to assess how nutrient intake patterns affect the aging process. Aging was quantified via blood biomarkers integrated to measure loss of homeostasis. Additionally, we extend nutritional geometry to 19 micronutrients. Salient results include benefits of intermediate protein and vitamin E intake. Broadly, we show that there are few simple answers of “good” or “bad” nutrients – optimal levels are generally intermediate, but dependent on other nutrients. Simpler linear/univariate analytical approaches are insufficient to capture such associations. We present an interactive tool to explore the results, and our approach presents a roadmap for future studies to explore the full complexity of the nutrition-aging landscape. Impact Statement Multidimensional nutritional analyses reveal how the association between diet and healthy aging is hard to untangle, as most nutrients have non-linear and interactive effects in humans.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".