| Literature DB >> 31193280 |
David C Nieman1, Laurel M Wentz2.
Abstract
This review summarizes research discoveries within 4 areas of exercise immunology that have received the most attention from investigators: (1) acute and chronic effects of exercise on the immune system, (2) clinical benefits of the exercise-immune relationship, (3) nutritional influences on the immune response to exercise, and (4) the effect of exercise on immunosenescence. These scientific discoveries can be organized into distinctive time periods: 1900-1979, which focused on exercise-induced changes in basic immune cell counts and function; 1980-1989, during which seminal papers were published with evidence that heavy exertion was associated with transient immune dysfunction, elevated inflammatory biomarkers, and increased risk of upper respiratory tract infections; 1990-2009, when additional focus areas were added to the field of exercise immunology including the interactive effect of nutrition, effects on the aging immune system, and inflammatory cytokines; and 2010 to the present, when technological advances in mass spectrometry allowed system biology approaches (i.e., metabolomics, proteomics, lipidomics, and microbiome characterization) to be applied to exercise immunology studies. The future of exercise immunology will take advantage of these technologies to provide new insights on the interactions between exercise, nutrition, and immune function, with application down to the personalized level. Additionally, these methodologies will improve mechanistic understanding of how exercise-induced immune perturbations reduce the risk of common chronic diseases.Entities:
Keywords: Aging; Exercise; Immunology; Infection; Inflammation; Mass Spectrometry; Nutrition
Year: 2018 PMID: 31193280 PMCID: PMC6523821 DOI: 10.1016/j.jshs.2018.09.009
Source DB: PubMed Journal: J Sport Health Sci ISSN: 2213-2961 Impact factor: 7.179
Fig. 1Key research areas and basic findings in exercise immunology.
Fig. 2Exercise immunology research can be organized into 4 distinctive periods.
Fig. 3Acute exercise stimulates the interchange of innate immune system cells and components between lymphoid tissues and the blood compartment. Although transient, a summation effect occurs over time, with improved immunosurveillance against pathogens and cancer cells and decreased systemic inflammation.
Fig. 4The contrast in acute immune responses to heavy exertion (e.g., a marathon race) and a 30- to 45-min walking bout. DTH = delayed-type hypersensitivity; IgA = immunoglobulin A; Ne/Ly = neutrophil/lymphocyte ratio; NK = natural killer; OB = oxidative burst.
Research on the relationship between vigorous exercise and illness.
| Investigator | Study population | Research design | Key finding |
|---|---|---|---|
| Peters and Bateman | 141 ultramarathon runners and 124 controls (aged 18–65 years) | Participants reported 2-week recall of illness symptoms after 56-km race | Illness incidence 2× higher in runners after race |
| Nieman et al. | 1828 marathon runners and 134 runner controls (aged 36.9 ± 0.2 years) | Participants reported illness symptoms 2 months before and 1 week after March 42.2-km race | Illness incidence 6× higher in runners who finished race |
| Heath et al. | 530 runners (aged 39.4 years) | Participants reported training log and illness symptoms every month for 1 year | Running >485 miles/year (780 km/year) increased risk of illness. |
| Konig et al. | 852 German athletes (aged 23.6 ± 9.5 years) | Participants retrospectively reported illness episodes over past 12 months | Illness incidence 2× higher in endurance sports (OR = 2.2); 2× higher with stress (OR = 2.0); and nearly 2× with sleep deprivation (OR = 1.7). |
| Spence et al. | 20 elite triathletes/cyclists, 30 recreational triathletes/cyclists, 20 sedentary controls (aged 18–34 years) | Participants followed for 5 months in summer/autumn; reported daily illness symptoms | Illness incidence 4× higher in elite athletes and 2× greater in controls |
| Gleeson et al. | 75 endurance trained university students (aged 18–35 years) | Participants followed for 4 months in winter; reported weekly illness symptoms | Greater illness incidence in high and medium |
| Rama et al. | 19 elite swimmers | Participants followed for 7 months in winter; reported daily illness symptoms | 67% of illness episodes occurred during high volume training in swimmers |
| Hellard et al. | 28 elite swimmers (aged 16–30 years) | Participants followed for 4 years; monitored weekly for illness | Illness increased 1.08× (95%CI: 1.01–1.16) every 10% increase in resistance training and 1.10× (95%CI: 1.01–1.19) for every 10% increase in high-load training. |
| Svendsen et al. | 42 elite cross-country skiers (aged 24 ± 4 years) | Participants followed for 8 years; reported illness symptoms daily for 10 days after the Tour de Ski race | Illness incidence was 3× higher in skiers who raced the Tour de Ski |
| Raysmith and Drew | 33 international track and field athletes | Participants reported illness symptoms during 6 months preceding competition for 5 years | Illness incidence was 23%; one-half of illnesses occurred 2 months before competition. Better performing athletes had a lower incidence of illness. |
| Drew et al. | 132 elite athletes preparing for the Olympics | 3 months before competition, participants reported illness symptoms during a 1-month time period | Illness symptoms in 100% athletes (46% upper respiratory). Risk factors were female sex, low energy availability. |
| Prien et al., | 1551 elite athletes preceding World Championship competition | Participants retrospectively reported illness symptoms during 4 weeks preceding competition | Illness incidence ranged from 5% to 13%. |
| Engebretsen et al., | 27,245 elite athletes during an international Olympic competition | Medical staff reported illness symptoms during competition event (<4 weeks) | Illness incidence ranged from 5% to 18%; Risk factor was female sex. |
| Mountjoy et al., | 5293 elite aquatics athletes during the international World Championships | Medical staff reported illness symptoms during competition event (<4 weeks) | Illness incidence ranged from 7% to 13%. |
| Alonso et al., | 3305 elite track and field athletes during the international World Championships | Medical staff reported illness symptoms during competition event (<4 weeks) | Illness incidence ranged from 2% to 7%; 10× greater illness incidence in endurance events |
Abbreviations: CI = confidence interval; OR = odds ratio; URTI = upper respiratory tract infections.
Research on the relationship between moderate exercise and illness.
| Investigator | Study population | Research design | Key finding |
|---|---|---|---|
| Nieman et al. | 36 mildly obese sedentary women (aged 34.4 ± 1.1 years) | Randomized to 15 weeks of moderate intensity (45 min/day × 5 days/week) walking program or observational control | Fewer days with illness symptoms reported in walkers |
| Nieman et al. | 32 sedentary women (aged 73.4 ± 1.2 years); 12 highly conditioned women (aged 72.5 ± 1.8 years) | Sedentary women randomized to a 12-week moderate intensity (30- to 40-min/day × 5 days/week) walking program or stretching (45 min/day × 5 days/week) in fall season | Illness incidence 8% in highly conditioned, 21% in walkers, and 50% in controls. |
| Nieman et al. | 91 obese women (aged 45.6 ± 1.1 years) | Randomized to a 12-week moderate intensity (45 min/day × 5 days/week) walking program or stretching 45 min/day × 4 days/week | Fewer days with illness symptoms reported in walkers |
| Chubak et al. | 115 postmenopausal women (aged 60.7 ± 6.9 years) | Randomized to 1 year of moderate intensity exercise (45 min/day × 5 days/week) or stretching control (45 min/day × 1 day/week) | Illness incidence 30% in exercise |
| Barrett et al | 373 male and female older adults (aged 59.3 ± 6.6 years (2012); 49.9 ± 11.8 years (2018)) | Randomized to 8 week moderate-intensity sustained exercise (group sessions; home practice) or observational control | Pooled datasets: proportional reductions of incidence, days-of-illness, and global severity were 14%, 23%, and 31% for exercise compared with controls. |
| Mathews et al. | 547 male and female adults (aged 48.0 ± 12.4 years) | Participants followed for 1 year; interviewed for physical activity and illness symptoms every 90 days | 29% decreased illness risk in upper |
| Fondell et al | 1509 male and female adults (aged 20–60 years) | Participants followed for 4 months; baseline questionnaire on physical activity; illness symptoms assessed every 3 weeks | 18% decreased illness risk in high |
| Nieman et al | 1002 male and female adults (aged 18–85 years) | Participants followed for 12 weeks in winter and autumn seasons; baseline questionnaire on physical fitness levels; daily illness symptoms checklist | 46% decrease in total day with illness in high |
| Zhou et al. | 1413 male and female adults (aged 38.9 ± 9.0 years) | Participants retrospectively reported frequency of illness and physical activity over the past year | 26% decreased illness risk in high |
Fig. 5J-curve model of the relationship between the exercise workload continuum and risk for upper respiratory tract infection (URTI). Other factors such as travel, pathogen exposure, sleep disruption, mental stress, and dietary patterns may influence this relationship. This figure was adapted from Nieman.
Fig. 6The upper tertiles of fitness and exercise frequency are associated with reduced numbers of days with upper respiratory tract infections (URTI). Data from Nieman et al.
Fig. 7C-reactive protein (CRP) and interleukin-6 (IL-6) values for obese and athletic groups (data expressed as mean ± SD). Data are from ongoing studies in the first author's lab during the past 2 decades. BMI = body mass index.
Research showing the effect of carbohydrates on inflammation and immune biomarkers after >90 min of endurance exercise.
| Investigator | Study population | Exercise protocol | Carbohydrate intervention | Postexercise immune response | |
|---|---|---|---|---|---|
| Nieman et al., | 30 male and female marathon runners (aged 41.5 ± 2.0 years) randomized to CHO or placebo | 2.5-h run at 75%–80%VO2max | 6% CHO or placebo beverage consumed before, during, and after exercise | CHO ↑ glucose; ↓ cortisol; ↓ total leukocytes, neutrophils, monocytes, and lymphocytes; ↓ IL-6, IL-1ra | |
| Nieman et al., | 10 male and female triathletes (aged 34.0 ± 2.1 years) in cross-over design | 2.5-h cycle and run at 75%VO2max | 6% CHO or placebo beverage consumed before, during, and after exercise | CHO ↑ glucose, insulin; ↓ cortisol, growth hormone; ↓ neutrophils, monocytes, lymphocytes; ↓ granulocyte, monocyte phagocytosis and oxidative burst activity; ↓ neutrophil/lymphocyte ratio; ↓ NK cell cytotoxicity; ↓ IL-6, IL-1ra | |
| Henson et al. | 15 Olympic female rowers (aged 22.4 ± 0.5 years) in a cross-over design | 2-h rowing session | 6% CHO or placebo beverage consumed before, during, and after exercise | CHO ↑ glucose; ↓ total leukocytes, neutrophils, and monocytes; ↓ phagocytosis; ↓ IL-1ra | |
| Nieman et al. | 16 marathon runners (aged 50.1 ± 1.5 years) in a cross-over design | 3-h run at 70%VO2max | 6% CHO or placebo beverage consumed before and during exercise | CHO ↑ glucose, insulin; ↓ cortisol; ↓ total leukocytes, monocytes, lymphocytes, and granulocytes; ↓ plasma IL-6, IL-10, IL-1ra; ↓ skeletal muscle IL-6, IL-8 mRNA | |
| Bishop et al | 9 trained male cyclists (aged 25.0 ± 2.0 years) in a cross-over design | 2-h cycle at 75%VO2max | 6.4% CHO or placebo beverage consumed before, during, and after exercise | CHO ↑ glucose; ↓ cortisol; ↓ neutrophils | |
| Keller et al. | 8 untrained men (aged 24.0 ± 1.0 years) in a cross-over design | 3-h cycle at 60% maximal workload | 6% CHO or placebo beverage consumed during exercise | CHO ↑ glucose; ↓ free fatty acids; ↓ plasma IL-6, ↓ adipose tissue IL-6 mRNA | |
| Febbraio et al. | 7 men (aged 22.1 ± 3.8 years) in cross-over design | 2-h cycle at 65%VO2max | 6.4% CHO or placebo beverage consumed before and during exercise | CHO ↑ glucose; ↓ free fatty acids; ↓ IL-6 | |
| Henson et al. | 48 male and female marathon runners (aged 42.5 ± 2.4 years) randomized to CHO or placebo | 42-km marathon | 6% CHO or placebo beverage consumed before and during exercise | CHO ↑ glucose, insulin; ↓ cortisol; ↓ total leukocytes, neutrophils, and monocytes | |
| Davison and Gleeson | 6 moderately trained men (aged 25.0 ± 2.0 years) in a cross-over design | 2.5-h cycle at 60%VO2max | 6% CHO or placebo beverage consumed before and during exercise | CHO ↑ glucose; ↓ cortisol; ↓ ACTH; ↓ total leukocytes, neutrophils; ↑ bacterial-stimulated neutrophil degranulation | |
| Lancaster et al. | 7 moderately trained men (aged 25.0 ± 1.0 years) in a cross-over design | 2.5-h cycle at 65%VO2max | 6.4%, 12.8% CHO or placebo beverage consumed before and during exercise | CHO ↑ glucose; ↓ cortisol; ↓ growth hormone; ↓ total leukocytes, neutrophils, monocytes; ↓ CD4+ T cell IFN-γ and CD8+ T cell IFN-γ lymphocytes. No significant difference between CHO concentrations. | |
| Li and Gleeson | 9 men (aged 28.7 ± 1.6 years) in a cross-over design | 90-min cycle at 60%VO2max | 10% CHO or placebo beverage consumed before and during exercise | CHO ↑ glucose; ↓ cortisol, epinephrine, ACTH, growth hormone; ↓ total leukocytes, monocytes, lymphocytes; ↓ IL-6 | |
| Nieman et al. | 15 trained male cyclists (aged 29.2 ± 6.0 years) in a cross-over design | 2.5-h cycle at 75%VO2max | 6% CHO or placebo beverage consumed before, during, and after exercise | CHO ↑ glucose, insulin; ↓ cortisol, epinephrine; ↓ total leukocytes, neutrophils; ↓ IL-6, IL-10, IL-1ra | |
| Nieman et al. | 12 trained male cyclists (aged 21.0 ± 1.0 years) in a cross-over design | 2-h cycle at 75%VO2max | 6% CHO or placebo beverage consumed before and during exercise | CHO ↑ glucose, insulin; ↓ cortisol; ↓ total leukocytes, neutrophils, and monocytes | |
| Scharhag et al. | 14 trained male cyclists/triathletes (aged 25.0 ± 5.0 years) in a cross-over design | 4-h cycle at 70% anaerobic threshold | 6%, 12% CHO, or placebo beverage consumed before and during exercise | 6% and 12% CHO ↑ glucose; ↓ cortisol; ↓ total leukocytes, neutrophils, and monocytes | |
| Nieman et al. | 14 trained male cyclists (aged 37.0 ± 7.1 years) in a cross-over design | 75-km time trial | 6% CHO beverage or matched CHO banana consumed before and during exercise | No difference in immune and inflammation measures (e.g., IL-6, granulocyte and monocyte phagocytosis) between banana and CHO beverage; higher FRAP and plasma dopamine with banana. | |
| Nieman et al. | 20 trained male cyclists (aged 39.2 ± 1.9 years) in a cross-over design | 75-km time trial | Banana, pear, or water consumed before and during exercise | Banana and pear ↑ glucose, RER; ↓ cortisol, IL-10; ↓ neutrophil/ lymphocyte ratio; ↑ antioxidant capacity (sulfated phenolics, FRAP), ↓ fatty acid mobilization and oxidation metabolites | |
| Shanely et al. | 20 trained male cyclists (aged 48.5 ± 2.3 years) in a cross-over design | 75-km time trial | 6% CHO beverage or matched CHO watermelon consumed before and during exercise | No difference in inflammation measures (e.g., cytokines and immune cell counts) between watermelon and CHO beverage; watermelon ↑ antioxidant capacity (FRAP, ORAC); ↑ citrulline, arginine, nitrate | |
| Nieman et al. | 20 trained male and female cyclists (aged 39.1 ± 2.4 years) in a cross-over design | 75-km time trial | 6% CHO beverage, 2 types of banana, or water consumed before and during exercise | Bananas and CHO beverage ↑ glucose, fructose; ↓ cortisol; ↓ IL-6, IL-10, IL-1ra; ↓ total leukocytes; ↓ 9+13 HODES; ↓ fatty acid mobilization and oxidation metabolites | |
Abbreviations: ACTH = adrenocorticotropic hormone; CHO = carbohydrate; COX2 = cyclo-oxygenase 2; CRP = C-reactive protein; FRAP = ferric reducing ability of plasma; HODES = hydroxyoctadecadienoic acid; IFN-γ = interferon gamma; IL-1ra = interleukin-1 receptor antagonist; mRNA = messenger ribonucleic acid; NK = natural killer; ORAC = oxygen radical absorbance capacity; RER = respiratory exchange ratio; VO2max = maximal oxygen uptake.
Fig. 8Carbohydrate ingestion before and during exercise attenuates postexercise inflammation.