Cross-sectional analysis of blood leukocyte responsiveness to interleukin-10 and interleukin-6 across age and physical activity level
Bibliographic record
Abstract
Abstract Objectives To determine how the anti-inflammatory actions of interleukin-10 (IL-10) and IL-6 differ across age and physical activity levels. Methods Using a cross-sectional design, fasted blood samples were obtained from younger physically inactive (YI: n=10, age: 22.7 ± 3.7 years, BMI: 24.8 ± 4.8 kg/m 2 , <150 min of weekly moderate-to-vigorous physical activity [MVPA]), younger highly active (YA: n=11 varsity cross country running athletes, 20.7 ± 2.7 years, 21.1 ± 1.8 kg/m 2 , >300 min of weekly MVPA), and older highly active (OA: 12, 56.0 ± 10.3 years, 22.8 ± 3.2 kg/m 2 , >300 min of weekly MVPA) individuals and analyzed for leukocyte counts, IL-10 and IL-6-related signaling, and cytokine secretion ex vivo. Results Total white blood cells and monocytes were similar between groups (p=0.8) but YA and OA had lower lymphocyte counts than YI (p<0.01). The ability of IL-10 (1 ng/mL) to phosphorylate signal transducer and activator of transcription 3 (STAT3) in CD14 monocytes was greater in YA vs. YI (p<0.03) despite YA having lower IL-10 receptor expression (p<0.01). IL-6 (10 ng/mL) mediated STAT3 phosphorylation in CD4 lymphocytes was higher in OA compared YI (p<0.01), with a similar tendency observed for YA vs. YI (p=0.08). Despite enhanced responsiveness of STAT3 to IL-10/6 in active individuals, the ability of IL-10/6 to inhibit tumor necrosis factor-alpha (TNF-⍺) secretion from lipopolysaccharide-stimulated whole-blood was similar between groups. Conclusions Highly active younger and older individuals demonstrate enhanced IL-10- and IL-6-mediated activation of immune cell STAT3. Although the ability of IL-10/6 to inhibit TNF-⍺ secretion appeared unimpacted by activity level, anti-inflammatory cytokine actions were preserved in older active individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".