Associations of fat‐soluble micronutrients and redox biomarkers with frailty status in the FRAILOMIC initiative
Bibliographic record
Abstract
Abstract Background A poor fat‐soluble micronutrient (FMN) and a high oxidative stress status are associated with frailty. Our aim was to determine the cross‐sectional association of FMNs and oxidative stress biomarkers [protein carbonyls (PrCarb) and 3‐nitrotyrosine] with the frailty status in participants older than 65 years. Methods Plasma levels of vitamins A (retinol), D 3 , E (α‐tocopherol and γ‐tocopherol) and carotenoids (α‐carotene and β‐carotene, lycopene, lutein/zeaxanthin, and β‐cryptoxanthin), PrCarb, and 3‐nitrotyrosine were measured in 1450 individuals of the FRAILOMIC initiative. Participants were classified into robust, pre‐frail, and frail using Fried's frailty criteria. Associations between biomarkers and frailty status were assessed by general linear and logistic regression models, both adjusted for cohort, season of blood sampling, gender, age, height, weight, and smoking. Results Robust participants had significantly higher vitamin D 3 and lutein/zeaxanthin concentrations than pre‐frail and frail subjects; had significantly higher γ‐tocopherol, α‐carotene, β‐carotene, lycopene, and β‐cryptoxanthin concentrations than frail subjects, and had significantly lower PrCarb concentrations than frail participants in multivariate linear models. Frail subjects were more likely to be in the lowest than in the highest tertile for vitamin D 3 (adjusted odds ratio: 2.15; 95% confidence interval: 1.42–3.26), α‐tocopherol (2.12; 1.39–3.24), α‐carotene (1.69; 1.00–2.88), β‐carotene (1.84; 1.13–2.99), lycopene (1.94; 1.24–3.05), lutein/zeaxanthin (3.60; 2.34–5.53), and β‐cryptoxanthin (3.02; 1.95–4.69) and were more likely to be in the highest than in the lowest tertile for PrCarb (2.86; 1.82–4.49) than robust subjects in multivariate regression models. Conclusions Our study indicates that both low FMN and high PrCarb concentrations are associated with pre‐frailty and frailty.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".