Acute, Exercise-Induced Alterations in Cytokines and Chemokines in the Blood Distinguish Physically Active and Sedentary Aging
Bibliographic record
Abstract
Aging results in a chronic, proinflammatory state which can promote and exacerbate age-associated diseases. In contrast, physical activity in older adults improves whole body health, protects against disease, and reduces inflammation, but the elderly are less active making it difficult to disentangle the effects of aging from a sedentary lifestyle. To interrogate this interaction, we analyzed peripheral blood collected at rest and postexercise from 68 healthy younger and older donors that were either physically active aerobic exercisers or chronically sedentary. Subjects were profiled for 44 low-abundance cytokines, chemokines, and growth factors in peripheral blood. At rest, we found that regular physical activity had no impact on the age-related elevation in circulating IL-18, eotaxin, GRO, IL-8, IP-10, PDGF-AA, or RANTES. Similarly, there was no impact of physical activity on the age-related reduction in VEGF, EGF, or IL-12 (p70). However, older exercisers had lower resting plasma fractalkine, IL-3, IL-6, and TNF-α compared to sedentary older adults. In contrast to our resting characterization, blood responses following acute exercise produced more striking difference between groups. Physically active younger and older subjects increased over 50% of the analyzed factors in their blood which resulted in both unique and overlapping exercise signatures. However, sedentary individuals, particularly the elderly, had few detectable changes in response to exercise. Overall, we show that long-term physical activity has a limited effect on age-associated changes in basal cytokines and chemokines in the healthy elderly, yet physically active individuals exhibit a broader induction of factors postexercise irrespective of age.
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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".