Table 2.
Characteristics as the type of study, aim, sample, design, and mean results of the studies.
| Author, Year | Type of Study | AIM | Sample | Study Design | Results |
|---|---|---|---|---|---|
| Pugh et al. (2017) [5] | Quasi-Experimental | Characterize the HIIT effects on small intestinal damage markers |
n = 11 (men runners trained) Aged 33.1 ± 10.4; VO2max 60.0 ± 3.2 mL/kg/min |
Acute HIIT episode markers of intestinal permeability and damage were evaluated and compared with resting conditions. Minimum running performance of 10 km (39 min) and a minimum of 5 workout sessions per week, using serum sampling, pre-exercise, after each set of exercises, and 2 h post-baseline |
HIIT significantly increased the serum lactulose: rhamnose ratio and sucrose concentrations compared with rest. In contrast, urinary lactulose: rhamnose or sucrose concentrations did not vary between study groups. Plasma I-FABP augmented in the recuperation period from HIIT only. After 24 h of HIIT, the researchers found mild symptoms of GI distress |
| Liang et al. (2019) [9] | Cross-sectional | Whether the intestinal microbiota is distinctive between higher-level and lower-level athletes |
n = 31 (professional martial arts athletes). 15 women and 16 men; aged 20–24 |
Martial arts athletes; Wushu routine, vigorous, fast and dynamic sports. The researchers used 16S rRNA gene sequencing to determine the intestinal changes |
Higher-level athletes have augmented metabolic capacity and diversity in the intestinal microbiota compared with lower-level athletes. |
| Petersen et al. (2017) [7] | Cross-sectional | Determine the presence of distinctive organisms in professional and amateur level competitive cyclists | n = 33 (professional and amateur level competitive cyclists); 11 women and 22 men; aged 19–49 | The study used metatranscriptomic (RNA-Seq) sequencing and mWGS |
The increase in Prevotella was associated with time reported exercising during an average week. Several professional cyclists have augmented levels of Methanobrevibacter smithii transcripts compared with amateur cyclists. |
| Bressa et al. (2017) [27] | Cross-sectional | Compare intestinal composition among two groups divided by physical exercise levels |
n = 40 (premenopausal women). 19 active and 21 sedentary Aged 18–40; BMI 20–25 kg/m2 |
The researchers used 16S rRNA gene sequencing to determine the intestinal changes | Performance of physical activity was associated with the presence of health-promoting bacteria (R. hominis, A. muciniphila, Bifidobacterium spp., and F. prausnitzii). Decreased levels of diversity were correlated with sedentary parameters |
| Karhu et al. (2017) [16] | Quasi-experimental | Evaluate the effect of running on GI function markers | n = 17 (active runners); 8 women and 9 men; aged 18–45 | The researchers measured secondary variables, such as zonulin, levels of serum intestinal I-FABP, and bacterial LPS, among others | Both, serum I-FABP and intestinal permeability increased after running, without differences amongst groups. No changes were observed in the bacterial LPS in serum |
| Keohane et al. (2019) [13] | Long-term | Analyze the changes in the intestinal microbiota of four well-trained male athletes to prolonged, high-intensity trans-oceanic rowing |
n = 4 (men athletes transatlantic rowing). Aged 25–27; BMI 23–25 kg/m2; VO2Max 46–50 mL/kg/min |
Metagenomic whole-genome shotgun sequencing was used | Intense exercise clearly impacts the diversity of the intestinal microbiota, with changes in specific bacteria related to metabolic pathways |
| Bycura et al. (2021) [31] | Quasi-experimental | Impact of CRE or RTE on intestinal microbiota |
n = 56 n = 28 CRE group (21 women; Aged 20.7; BMI 24.5 kg/m2 and 7 men; aged 20; BMI 24.0 kg/m2. n = 28 RTE group (17 women; aged 20.4; BMI 23.2 kg/m2 and 11 men; aged 22.6; BMI 24.59 kg/m2 |
Intestinal microbiota was measured using 16S rRNA gene sequencing | The observed changes were associated only with the CRE group, resulting in disturbance of the intestinal microbiota |
| Morishima et al. (2020) [18] | Cross-sectional | Effects of highly intensive endurance exercise on the intestinal microbiota and its relationship with the onset of the exercise-induced GI disorders |
n = 29 (15 women Japanese endurance runners and 14 nonathletic but healthy women). Aged 20–21; BMI 20.7–21.9 kg/m2 |
Fecal microbiota was tested using 16S rRNA metagenomics, and other variables such as moisture content, organic acids, and putrefactive metabolites concentrations were examined | Female elite endurance runners have more abundance of Faecalibacterium, and these changes could be associated with the succinate concentration in this group |
| Tota et al. (2019) [19] | Long-term | Evaluate intestinal and muscle damage in triathletes |
n = 15 (triathletes). Aged 6–14; VO2max 58.8 ± 4.5 mL/kg/min |
Variables used for the analysis were: cortisol, c-reactive protein, zonulin, and TNF-α | Zonulin and variables of permeability were augmented after the race |
| Zhao et al. (2018) [22] | Quasi-experimental | The gut microbiota immediately responds to the enteric changes in amateur half-marathon runners |
n = 20 (4 women and 16 men amateur half-marathon runners). Aged 31.3; BMI 22.6 kg/m2 |
Fecal samples were analyzed before and after the marathon using 16 rDNA sequencing analyses | Coriobacteriaceae changes were related to the exercise role in avoiding disease and refining health outcomes. |
| Moitinho-Silva et al. (2021) [34] | Randomized controlled trial | Analyze the changes in the intestinal microbiota on previously physically inactive, healthy adults in comparison to controls that did not perform regular exercise | n = 36 (11 controls; 13 endurance group; 12 strength group). Aged 22–41.3; BMI 19.7–32.5 kg/m2 | Fecal microbiota was tested using 16S rRNA metagenomics | Mucosal damage and inflammation were found after short-term resistance training. No changes were observed in intestinal microbiota |
| Sadowska-Krepa et al. (2021) [29] | Quasi-experimental | Evaluate intestinal damage in middle-aged male subjects |
n = 10 (amateur long-distance runners). Aged 21–35 |
Variables used for the analysis were: TAS, TOS/TOC, hs-CRP, I-FABP, and zonulin | After the exercise, the levels of intestinal permeability biomarkers as, hs-CRP, I-FABP, zonulin, and inflammation were augmented |
| Kulecka et al. (2020) [33] | Quasi-experimental | Evaluate differences in intestinal microbiota amongst healthy controls and endurance athletes |
n = 71 n = 14 marathon runners; n = 11 cross-country skiers; n = 46 healthy control individuals |
Fecal microbiota was tested using 16S rRNA metagenomics | Excessive training is associated with changes in Bacteroides and Prevotella and bacterial diversity |
| Tabone et al. (2021) [30] | Quasi-experimental | Determine whether the changes are driven by exercise on the gut microbiota (with 16S rRNA gene) and the serum and fecal metabolome |
n = 40 (men endurance cross-country runners). Aged 35.8 ± 8.0; BMI 22.8 ± 2.1 kg/m2; VO2max 58.8 ± 3.24 mL/kg/min |
Fecal microbiota was tested using 16S rRNA metagenomics | The changes in gut microbiota could be related to physiological changes in ammonia, uric acid, and lactate |
| Barton et al. (2017) [28] | Cross-Sectional | Evaluate differences in intestinal microbiota amongst exercise and a more sedentary state |
n = 86 (40 men professional international rugby union players and 46 men controls) | Fecal microbiota was tested using 16S rRNA metagenomics | Professional international rugby union players had more favorable effects in metabolic pathways than the control group |
| Craven et al. (2021) [32] | Quasi-experimental | Evaluate differences in intestinal microbiota according to training volume |
n = 14 (highly trained middle-distance runners). n = 6 women; aged 22.0 ± 3.4; VO2max 59.0 ± 3.2 mL/kg/min n = 8 men; aged 20.7 ± 3.2; VO2max 70.1 ± 4.3 mL/kg/min |
Fecal microbiota was tested using 16S rRNA metagenomics | No changes were observed in intestinal microbiota according to training volume in upper taxons. Changes in family, genus, and species were observed, these changes did not return to pre-levels |
Abbreviations: AFT, after fecal; BEF, before fecal; BMI, body mass index; CRE, cardiorespiratory exercise; DS, standard deviation; FCCS, female cross-country skiers; FDR, false discovery rate; FMR, female marathon runners; GI, gastrointestinal; HIIT, high-intensity interval training; hs-CRP, High-sensitivity C-reactive protein; HvolTr, high-volume training; I-FABP, intestinal fatty acid-binding protein; kg/m2, kilogram per square meter; LPS, lipopolysaccharide; MCCS, male cross-country skiers; MCHC, mean corpuscular hemoglobin concentration; mL/kg/min, milliliters per minute per kilogram; MMR, male marathon runners; mWGS, metagenomic whole genome shotgun; NormTr normal training; PGM, personal genome machine; PWC, physical working capacity; rRNA, ribosomal ribonucleic acid; RTE, resistance training exercise; TaperTr, exponential reduction in training; TAS, total antioxidant status; TOC, total oxidant capacity; TOS, total oxidant status; VO2max, the maximum amount of oxygen; WHO, World Health Organization; WSER, Western States Endurance Run.