| Literature DB >> 35954878 |
Romina Bonomini-Gnutzmann1, Julio Plaza-Díaz2,3,4, Carlos Jorquera-Aguilera1, Andrés Rodríguez-Rodríguez5, Fernando Rodríguez-Rodríguez6.
Abstract
(1) Background: The gut microbiota might play a part in affecting athletic performance and is of considerable importance to athletes. The aim of this study was to search the recent knowledge of the protagonist played by high-intensity and high-duration aerobic exercise on gut microbiota composition in athletes and how these effects could provide disadvantages in sports performance. (2)Entities:
Keywords: adults; aerobic exercise; elite athletes; gut microbiota; large intestine
Mesh:
Year: 2022 PMID: 35954878 PMCID: PMC9368618 DOI: 10.3390/ijerph19159518
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Search strategy in databases.
| Database | Search Strategy | Limits | Filters |
|---|---|---|---|
| Web of Science | (ALL (Physical activity AND gut microbiota OR Physical activity AND intestinal barrier OR Physical activity AND intestinal permeability OR Physical exercise AND gut microbiota OR Physical exercise AND intestinal barrier OR Physical exercise AND intestinal permeability)) | Title | 238 items filtered |
| PubMed | (Physical activity OR physical exercise) AND (gut microbiota OR intestinal barrier OR intestinal permeability) | Title | 104 items filtered |
| Scopus | TITLE-ABS-KEY (physical AND activity AND gut AND microbiota) OR (physical AND activity AND intestinal AND barrier) OR (physical AND activity AND intestinal AND permeability) OR (physical AND exercise AND gut AND microbiota) OR (physical AND exercise AND intestinal AND barrier) OR (physical AND exercise AND intestinal AND permeability) AND (LIMIT-TO (OA, “all”)) AND (LIMIT-TO (PUBYEAR, 2022) OR LIMIT-TO (PUBYEAR, 2021) OR LIMIT-TO (PUBYEAR, 2020) OR LIMIT-TO (PUBYEAR, 2019) OR LIMIT-TO (PUBYEAR, 2018) OR LIMIT-TO (PUBYEAR, 2017) OR LIMIT-TO (PUBYEAR, 2016) OR LIMIT-TO (PUBYEAR, 2015)) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”)) | Title | 5934 items filtered |
Figure 1Flowchart of articles through the search process.
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) [ | Quasi-Experimental | Characterize the HIIT effects on small intestinal damage markers | 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, | 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) [ | Cross-sectional | Whether | Martial arts athletes; Wushu routine, vigorous, fast and dynamic sports. | Higher-level athletes have augmented metabolic capacity and diversity in the intestinal microbiota compared with lower-level athletes. | |
| Petersen et al. (2017) [ | Cross-sectional | Determine the presence of distinctive organisms in professional and amateur level competitive cyclists | The study used metatranscriptomic (RNA-Seq) sequencing and | The increase in | |
| Bressa et al. (2017) [ | Cross-sectional | Compare intestinal composition among two groups divided by physical exercise levels | 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 ( | |
| Karhu et al. (2017) [ | Quasi-experimental | Evaluate the effect of running on GI function markers | 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) [ | Long-term | Analyze the changes in the intestinal microbiota of four well-trained male athletes to prolonged, high-intensity trans-oceanic rowing | 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) [ | Quasi-experimental | Impact of CRE or RTE on intestinal microbiota | 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) [ | Cross-sectional | Effects of | 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 | |
| Tota et al. (2019) [ | Long-term | Evaluate intestinal and muscle damage in triathletes | 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) [ | Quasi-experimental | The gut microbiota immediately responds to the enteric changes in amateur half-marathon runners | Fecal samples were analyzed before and after the marathon using 16 rDNA sequencing analyses | ||
| Moitinho-Silva et al. (2021) [ | 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 | 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) [ | Quasi-experimental | Evaluate intestinal damage in middle-aged male subjects | 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) [ | Quasi-experimental | Evaluate differences in intestinal microbiota amongst healthy controls and endurance athletes | Fecal microbiota was tested using 16S rRNA metagenomics | Excessive training is associated with changes in | |
| Tabone et al. (2021) [ | Quasi-experimental | Determine whether the changes are driven by exercise | 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) [ | Cross-Sectional | Evaluate differences in intestinal microbiota amongst | 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) [ | Quasi-experimental | Evaluate differences in intestinal microbiota according to training volume | 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.
Checklist from Joanna Briggs Institute’s criterium according to kind of study, percentage of criterium reached, and quality level of evidence.
| Criteriums According to Kind of Study | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Authors | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | Percentage Reached | Quality Level |
| Pugh et al. [ | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 66.7 | MQ | ||||
| Liang et al. [ | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 75.0 | HQ | |||||
| Petersen et al. [ | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 75.0 | HQ | |||||
| Bressa et al. [ | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 100.0 | HQ | |||||
| Karhu et al. [ | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 55.6 | MQ | ||||
| Keohane et al. [ | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 87.5 | HQ | |||||
| Bycura et al. [ | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 88.9 | HQ | ||||
| Morishima et al. [ | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 50.0 | MQ | |||||
| Tota et al. [ | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 50.0 | MQ | |||||
| Zhao et al. [ | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 55.6 | MQ | ||||
| Moitinho-Silva et al. [ | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 69.2 | MQ |
| Sadowska-Krepa et al. [ | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 55.6 | MQ | ||||
| Kulecka et al. [ | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 88.9 | HQ | ||||
| Tabone et al. [ | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 66.7 | MQ | ||||
| Barton et al. [ | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 75.0 | HQ | |||||
| Craven et al. [ | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 55.6 | MQ | ||||
HQ: high quality; MQ: medium quality.
Figure 2Interaction of different intensities and duration of exercise on the intestinal microbiota. Abbreviations: CRP, C-reactive protein; hs-CRP, high-sensitivity C-reactive protein; I-FABP, intestinal fatty-acid binding protein; IL, interleukin.