The Impact of Acute and Chronic Exercise on Immunoglobulins and Cytokines in Elderly: Insights From a Critical Review of the Literature
Bibliographic record
Abstract
The level of immunoglobulins and cytokines changes with an ageing immune system. This review summarizes findings from studies that have examined the impact of acute and chronic exercise on immunoglobulins and cytokines in the elderly. Our literature analysis revealed that acute endurance exercise resulted in increased secretory salivary immunoglobulin A (SIgA), while acute bouts of muscle strengthening exercise (i.e., isokinetic, eccentric, knee extensor exercise) increased plasma/muscle interleukin (IL)-6, IL-8 and tumor necrosis factor alpha (TNF-α) levels. Chronic exercise in the form of short-term endurance training (i.e., 12-16 weeks) and long-term combined endurance and resistance training (i.e., 6-12 months) induced increases in salivary SIgA concentration. We additionally identified that short-term endurance training at moderate intensities and the combination of endurance, strength, balance, and flexibility training increase plasma IL-10 and reduce plasma IL-6 and TNF-α in healthy elderly adults and male patients with chronic heart failure. Strength training for 6-12 weeks did not alter plasma IL-1β, IL-2, IL-6 and TNF-α concentration in healthy elderly adults and patients with chronic-degenerative diseases, while 12 weeks of resistance training decreased muscle TNF-α mRNA in frail elderly individuals. Short-term (i.e., 10-24 weeks) moderate- to high-intensity strength training reduced LPS-IL-6, LPS, IL-1β, LPS-TNF-α and circulating concentrations of TNF-α and increased IL-10 in healthy elderly women and older people with cognitive impairment, respectively. In conclusion, it appears that acute bouts of endurance exercise and short-term chronic exercise training exercise are appropriate methods to enhance mucosal immune function, reduce systemic markers of inflammation, and promote anti-inflammatory processes in elderly individuals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".