Effects of exercise combined with caloric restriction on inflammatory cytokines
Bibliographic record
Abstract
Chronic inflammation has been implicated in the pathogenesis of several chronic diseases, such as atherosclerosis and diabetes, as well as certain types of cancers. It has been suggested that circulating biomarkers for inflammation may be modified by exercise; however, few laboratory-based studies have been conducted in nonobese premenopausal women. The purpose of this investigation was to determine the impact of a 4-month exercise training and caloric-restriction intervention with the goal of weight loss on circulating biomarkers of inflammation in sedentary premenopausal women aged 25- 40 years (weight, 57 ± 2 kg). Subjects were studied for 6 consecutive menstrual cycles: 1 Screening, 1 Baseline, then 4 interventions (Interventions 1-4). Supervised aerobic training, consisting primarily of treadmill running and elliptical machine exercise, was performed 4 times per week for 40-90 min at 79% ± 0.7% of maximal heart rate. Subjects also consumed 30% fewer calories vs. baseline (1863 ± 58 to 1428 ± 53 kcalday(-1) (1 kcal = 4.186 kJ), p < 0.0001). Circulating inflammatory biomarkers, including adiponectin, high-sensitivity (hs) C-reactive protein (CRP), tumour necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), interferon-gamma (IFN-γ), and leptin, as well as body composition, aerobic capacity, and energy balance, were measured before and after the intervention. Maximal aerobic capacity increased by 8.5 ± 1.7 mL kg(-1)min-1 (p < 0.001) and body mass declined by an average of 3.7 ± 0.5 kg (p < 0.001). Significant reductions in IL-6 (0.39 ± 0.04 to 0.30 ± 0.03 pgmL(-1), p = 0.025), IFN-γ (0.58 ± 0.83 to 0.42 ± 0.64 pgmL-1, p = 0.030), and leptin (13.18 ± 1.28 to 6.28 ± 0.71 pgmL(-1), p < 0.001) were detected in response to the intervention. No significant changes in adiponectin, hs-CRP, or TNF-α were found. Weight loss in response to exercise training and caloric restriction is effective in reducing inflammatory markers, specifically IL-6 and leptin.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".