Intake of dietary advanced glycation end products influences inflammatory markers, immune phenotypes, and antiradical capacity of healthy elderly in a little‐studied population
Bibliographic record
Abstract
Abstract Dietary advanced glycation end products (dAGE) have profound negative effects on overall health, and their intake must be assessed. In this cross‐sectional study, we investigated dAGE intake of 337 adult participants (180/157:M/F; age range 50–73 years). Data were collected on anthropometrics, body composition, dietary intake, selected blood biochemistry, immunological parameters, and antiradical capacity (50% hemolysis time; HT 50 ). From the dietary data, dAGEs and phytochemical index (PI) were calculated. Mean BMI, % body fat (%BF), and fasting plasma glucose were all within the accepted normal range. Subjects with high dAGE intake had higher %BF, higher energy intake, and lower PI. They tended to have lower CD4/CD8 ratios and higher proportions of B cells and NK cells, but had significantly higher hs‐CRP levels and lower HT 50 values. Results on HT 50 suggested that being >60 years of age enhanced dAGE‐associated impairment of defense capacity in both those with low and high HT 50 compared with those <60 years of age. Thus, overall dAGE consumption was high, but elderly participants had lower dAGE intake than younger adults. Indicators of nutritional status and immunological parameters of the subjects were found to be associated with dAGE intake, suggesting a potential impact on health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".