Frailty markers comprise blood metabolites involved in antioxidation, cognition, and mobility
Bibliographic record
Abstract
As human society ages globally, age-related disorders are becoming increasingly common. Due to decreasing physiological reserves and increasing organ system dysfunction associated with age, frailty affects many elderly people, compromising their ability to cope with acute stressors. Frail elderly people commonly manifest complex clinical symptoms, including cognitive dysfunction, hypomobility, and impaired daily activity, the metabolic basis of which remains poorly understood. We applied untargeted, comprehensive LC-MS metabolomic analysis to human blood from 19 frail and nonfrail elderly patients who were clinically evaluated using the Edmonton Frail Scale, the MoCA-J for cognition, and the TUG for mobility. Among 131 metabolites assayed, we identified 22 markers for frailty, cognition, and hypomobility, most of which were abundant in blood. Frailty markers included 5 of 6 markers specifically related to cognition and 6 of 12 markers associated with hypomobility. These overlapping sets of markers included metabolites related to antioxidation, muscle or nitrogen metabolism, and amino acids, most of which are decreased in frail elderly people. Five frailty-related metabolites that decreased-1,5-anhydroglucitol, acetyl-carnosine, ophthalmic acid, leucine, and isoleucine-have been previously reported as markers of aging, providing a metabolic link between human aging and frailty. Our findings clearly indicate that metabolite profiles efficiently distinguish frailty from nonfrailty. Importantly, the antioxidant ergothioneine, which decreases in frailty, is neuroprotective. Oxidative stress resulting from diminished antioxidant levels could be a key vulnerability for the pathogenesis of frailty, exacerbating illnesses related to human aging.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".