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 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 | 0.000 | 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 | 0.001 |
| 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".