Variance in the acute inflammatory response to prolonged cycling is linked to exercise intensity
This study investigated the influence of age, body composition, physical fitness, training volume and intensity, and underlying systemic inflammation on exercise-induced inflammation and innate immune function in a heterogeneous group of cyclists. Subjects included 31 male cyclists (mean ± standard deviation, age 38.8 ± 10.6 years, body fat 17.8%± 5.6%, VO(2max) 55.8 ± 8.4 mL kg(-1) min(-1)) who cycled for 1.75 h at 60% watts(max) followed by a 10-km time trial (18.3 ± 0.3 min). Blood samples were collected pre-, post-, and 1-h-postexercise, and analyzed for WBCs, 9 cytokines [interleukin (IL)-6, tumor necrosis factor-alpha, granulocyte-macrophage colony-stimulating factor, interferon-γ, IL-1β, IL-2, IL-8, IL-10, and IL-12p70], and granulocyte and monocyte phagocytosis (GR-PHAG and MO-PHAG) and oxidative burst activity (GR-OBA and MO-OBA). Exercise-induced changes varied widely, with significant increases measured for 8 of 9 cytokines, GR-PHAG (mean change 99%) (95% confidence limits, 69%, 128%) and MO-PHAG (43%) (28%, 58%), and WBC (160%) (133%, 187%), and decreases for GR-OBA (-30%) (-43%,-16%) and MO-OBA (-23%) (-36%,-10%). Correlation and stepwise regression analysis revealed that changes in these variables were not related to age, body fat percentage, VO(2max), training volume, or pre-exercise C-reactive protein. Performance measures, specifically the average heart rate and rating of perceived exertion, were correlated with changes in several variables, including IL-8 (r=0.68 and 0.67, respectively, P<0.001) and IL-6 (r=0.51, P=0.004, and r=0.46, P=0.011, respectively). In summary, variance in exercise-induced inflammation and innate immunity in male cyclists in response to 2 h of endurance exercise is best explained by exercise intensity.
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组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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