Serum Proteomic and Metabolomic Signatures of High Versus Low Physical Function in Octogenarians
Bibliographic record
Abstract
Physical function declines with aging, yet there is considerable heterogeneity, with some individuals declining very slowly while others experience accelerated functional decline. To gain insight into mechanisms promoting high physical function with aging, we performed proteomics, targeted metabolomics, and targeted kynurenine-focused metabolomic analyses on serum specimens from three groups of octogenarians: High-functioning master athletes (HF, n = 16), healthy normal-functioning non-athletes (NF, n = 12), and lower functioning non-athletes (LF, n = 11). Higher performance status was associated with evidence consistent with: Lower levels of circulating proinflammatory markers, as well as unperturbed tryptophan metabolism, with the normal function of the kynurenic pathway; higher circulating levels of lysophosphatidylcholines that have been previously associated with better mitochondrial oxidative capacity; lower activity of the integrated stress response; lower levels of circulating SASP protein members; and lower levels of proteins that reflect neurodegeneration/denervation. Extending the observations of previous studies focused on the biomarkers of aging that predict poor function, our findings show that many of the same biomarkers associated with poor function exhibit attenuated changes in those who maintain a high function. Because of the cross-sectional nature of this study, results should be interpreted with caution, and bidirectional causality, where physical activity behavior is both a cause and outcome of differences in the biomarker changes, remains a possible interpretation.
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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 | 0.001 |
| 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 | 0.000 | 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".