Association between physical activity and inflammatory markers in community-dwelling, middle-aged adults
Bibliographic record
Abstract
Physical activity has been known to deter inflammatory process; yet, the evidence is scarce in healthy, middle-aged population. We assessed the association between physical activity and inflammatory biomarkers, including high sensitivity (hs) C-reactive protein, interleukin (IL)-1α, -1β, and -6, tumor necrosis factor (TNF) -α and -β, and monocyte chemotactic protein (MCP) -1 and -3. Functional and leisure-time physical activity was assessed by the International Physical Activity Questionnaire. Inflammatory biomarkers were measured by multiplex enzyme-linked immunosorbent assay. Compared with highly physically active participants based on total metabolic equivalent of task, the most sedentary group had significantly higher odds ratio and [95% confidence interval] for ≥75th percentile of TNF-α (1.64 [1.10–2.44]), TNF-β (1.50 [1.09–2.07]), IL-1β (2.14 [1.49–3.09]), hsIL-1β (1.72 [1.15–2.58]), IL-6 (1.84 [1.24–1.73]), hsIL-6 (2.05 [1.35–3.12]), and MCP-1 (1.91 [1.28–2.87]) levels. Results for IL-1α and MCP-3 were inconsistent, as the least active group had lower odds for above the median IL-1α (0.65 [0.49–0.95]) and MCP-3 (0.71 [0.54–0.93]) yet higher odds for ≥75th percentile IL-1α (2.36 [1.63–3.42]) and MCP-3 (2.44 [1.63–3.64]) levels. Based on duration of moderate-to-vigorous physical activity, sedentary participants had significantly higher odds for above median (1.40 [1.13–1.73]) and ≥75th percentile (1.33 [1.00–1.77]) IL-1β compared with those fulfilling the guideline recommendation. Subgroup analyses showed minimal sex differences. Routine inflammatory assessment may help to achieve primordial prevention of cardiovascular and metabolic diseases. Novelty: Healthy, middle-aged adults with physically active lifestyle were generally at lower odds for elevated inflammatory status. The associations persisted regardless of sex, age, comorbidities, adiposity, and diet.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".