The intensive physical activity causes changes in the composition of gut and oral microbiota

Abstract

This study aimed to compare the gut and oral microbiota composition of professional male football players and amateurs. Environmental and behavioral factors are well known to modulate intestinal microbiota composition. Active lifestyle behaviors are involved in the improvement of metabolic and inflammatory parameters. Exercise promotes adaptational changes in human metabolic capacities affecting microbial homeostasis. Twenty professional football players and twelve amateurs were invited to the study groups. Fecal and oral microbiota were analyzed using next-generation sequencing of the 16S rRNA gene. Diversity in the oral microbiota composition was similar in amateurs and professionals, while the increase in training intensity reduced the number of bacterial species. In contrast, the analysis of the intestinal microbiota showed the greatest differentiation between professional football players and amateurs, especially during intensive training. Firmicutes were characterized by the largest population in all the studied groups. Intensive physical activity increases the abundance of butyrate and succinate-producing bacteria affecting host metabolic homeostasis, suggesting a very beneficial role for the host immune system's microbiome homeostasis and providing a proper function of the host immune system.

Introduction

Professional athletes are characterized by their unique metabolism and physiology compared to their sedentary counterparts. Exposing their bodies to extreme physiological demands triggers a cascade of adaptation processes, including alternating muscles' functions, rebalancing electrolytes, and synthesizing acute-phase proteins that trigger systemic inflammation and immune response¹. It is well known that active lifestyle behaviors improve the metabolic and inflammatory condition of the human body. Even exercise regimes are recommended for many chronic diseases and play a role in therapeutic strategies, for example, against obesity. The beneficial effect of physical activity is mainly related to the influence on adaptational metabolic changes stabilizing intestinal microbiota. While exercise has well-known effects on the immune system and metabolism, little is known regarding its influence on digestive tract microbiota. The digestive system’s microbiome is a collection of 500–1000 species of microorganisms, mainly bacteria and fewer archaea, fungi, protozoans, and a large population of viruses, living along the digestive tract, from the mouth to the colon of the host organism.

Multiple microbiome influences on the human body were discovered. The composition of the digestive system’s microbiome undergoes ongoing changes during the host’s lifetime. Microbiota begins to form at birth, and its composition changes with age². It evolves throughout a person's life thanks to endogenous (infections, genetic factors, age of host) and exogenous factors (stress, medicaments, environment). During physical activity, not only human cells undergo metabolic changes but also the microorganisms that build up the microbiota.

The number of studies examining the microbiota’s influence on the organism's physical efficiency is low. Research on laboratory animals and humans shows an increase in the microbiome’s diversity with regular physical activity³. Nowadays, it is believed that greater diversity and richness of the microbiome are critical for healthy gut microbiota composition. The optimal healthy microbiota composition is proposed to differ between individuals⁴. By fermentation of polysaccharides, the intestinal microbiome produces short-chain acids, which are then used as an energy source by the host organism⁵. The potential pathways responsible for those connections include modulation of Toll-Like Receptors, higher production of SCFAs due to AMPK activation, altered lymphocyte composition, and the modification of the gut transit time⁶. Additionally, increased protein consumption positively impacts microbial diversity⁷.

This study aimed to investigate whether the intensity of physical activity affects the composition of the oral and intestinal microbiota of professional football players and compare the composition of both microbiotas between groups with different levels of activity—professional football players and amateurs.

Results

Alpha and beta diversity of gut and oral microbiota of football players

Data analysis showed that the alpha diversity of oral microbiota in amateur and professional football players was similar (Figs. 1 and 2). The Chao1 (p = 0.796) and ACE (p = 0.886) alpha diversity indices were not significantly different when compared between the two groups. However, the oral microbiota alpha diversity was lower during the high-activity period than in the low-activity period (Fig. 2A,B), according to Chao1 (p = 0.004), and ACE (p = 0.003) indices. The alpha diversity of gut microbiota was different. Among professional football players, significantly higher gut microbiota diversity was observed than in the amateurs, with a p-value less than 5% for Chao1 and p = 0.036 for ACE, respectively (Fig. 1C,D). Analysis of data revealed training intensity and the significant differences in richness based on Chao1 (p = 0.006) and ACE (p = 0.004) indices (Fig. 2C,D).

Fig. 1.

The summary of alpha diversity index. Chao 1 and ACE species indexes represent community richness. The (A) and (B) figures represent oral, whereas (C) and (D) are the gut microbiota profiles. Red boxes represent amateurs, and blue boxes professional football players. The alpha diversity was statistically significant only within the OTUs of the gut (p = 0.005) (Chao1), and the ACE diversity index (p = 0.036). * p < 0.05 (Student's t-test) represents a significant level.

Fig. 2.

The summary of alpha diversity index. Chao 1 and ACE species indexes represent community richness. The (A) and (B) figures represent oral, whereas (C) and (D) are the gut microbiota profiles. Red boxes represent microbiota profiles before, whereas blue ones represent the state after extensive football training. The alpha diversity was statistically significant, showing the differences (before and after intensive football training) within the OTUs of the oral (p = 0.004) (Chao1), and the ACE diversity index (p = 0.003) and gut (p = 0.006) (Chao1), and the ACE (p = 0.004). * p < 0.05 (Student's t-test) represents a significant level.

The beta diversity was demonstrated as PCoA based on the Bray–Curtis algorithm in all groups (Figs. 3, 4, and 5). The index did not indicate the statistical differences between professional football players and amateurs in oral microbiota composition (p = 0.390), as shown in Fig. 1A,B. However, in the oral microbiome, there were significant dissimilarities in the composition of microbial communities in two periods with intensive and non-intensive training. In these groups index Bray–Curtis was measured as statistically significant with a p-value = 0.001. In contrast, statistical differences were detected in the gut microbiota of professional football players when compared to the amateurs (p = 0.001) (Fig. 3C,D). In contrast, statistical differences were detected in the gut microbiota of professional football players when compared to the amateurs, but statistical differences were detected in the gut microbiota of professional football players when compared to the amateurs (p = 0.001).

Fig. 3.

The principal coordinates analysis (PCoA) shows beta diversity of oral (A) and gut (B) microbiota in amateur and professional football players. Data are plotted based on two components described as 20.6% (PCo1 on Axis1) and 19.1% (PCo2, on Axis 2) of oral microbiota variation (A), and 19.4% (PCo1 on Axis1) and 13.6% (PCo2, on Axis 2) of gut variation (B). One sample is represented by one point.

Fig. 4.

Beta diversity measurement, based on Bray–Curtis (A), Jaccard (B), and Jensen–Shannon divergence (C), shows non-statistically significant differences in the composition of microbial communities in the gut microbiota before and after intensive training. The statistical significance was measured using the PERMANOVA method. The p-value was calculated = 0.001.

Fig. 5.

Beta diversity measurement, based on Bray–Curtis (A), Jaccard (B), and Jensen–Shannon divergence (C), shows statistically significant differences in the composition of microbial communities in the oral microbiota before and after intensive training. The statistical significance was measured using the PERMANOVA method. P-value was calculated as statistically significant = 0.001.

Abundance profiling of oral and gut microbiota

OUT data were summarized and compared to their abundance at different taxonomic levels (phylum, class, order, family, genus, and species). The results were demonstrated as merged stacked bars based on each group’s median Percentage Abundance (Figs. 6 and 7). Oral microbial profiles in professional football players were characterized by a higher abundance of bacteria phyla Firmicutes (38%), Proteobacteria (26%), Actinobacteria (16%), Bacteroidetes (11%), and Fusobacteria (7%). These results of actual abundance were not statistically significant with the cut-off of p-value equal to 0.05 when compared to the percentage abundance observed in the amateurs as follows: Firmicutes (39%), Proteobacteria (23%), Actinobacteria (18%), Bacteroidetes (13%), and Fusobacteria (6%).

Fig. 6.

The stacked bars show the abundance profiling based on the percentage abundance at the phylum taxonomic level. Figure (A) represents the percentage abundance in the oral cavity and B in the intestines. As previously used, the L symbol is reserved for professional football players, whereas A for amateurs. Only six top taxa were shown, and the rest taxa were merged into Others.

Fig. 7.

The stacked bars show the abundance profiling based on the percentage abundance at the class taxonomic level. Figure (A) represents the percentage abundance in the oral cavity and (B) in the intestines. The L symbol is reserved for professional football players, whereas A for amateurs. Only six top taxa were shown, and the rest taxa were merged into Others.

In the gut, the most abundant bacterial phyla were Firmicutes (76%), followed by Bacteroidetes (16%) and Actinobacteria (7%). These results were statistically significant, with p = 0.01. In contrast, the gut microbiota profiles in the amateurs were characterized by a statistically significant higher abundance of bacteria phyla Bacteroidetes (25%) and a lower abundance of Firmicutes (65%) compared to the gut microbiota of the professional football players (Fig. 6).

When comparing bacterial genera, higher abundances in genera were observed in professional football players in the gut and oral microbiota, as shown in Figs. 6 and 7A,B. Intensive physical activity was associated with a decreased abundance of bacteria Firmicutes, Bacteroidetes, and Actinobacteria in the gut (Fig. 7A,B). A similar trend was noticed in the abundance profiles of the oral microbiota. Increased physical activity influenced the decrease in the abundance of the top five taxa such as Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, and Fusobacteria (Fig. 7A,B).

When comparing bacterial genera, higher abundances of bacterial genera was shown in professional football players both in the gut and oral microbiota, as shown in Fig. 8

Fig. 8.

The stacked bars show the abundance profiling based on the percentage abundance at the species taxonomic level. Figure (A) represents the percentage abundance in the oral cavity and (B) in the intestines. The L symbol is reserved for professional football players, whereas (A) for amateurs. Only six top taxa were shown, and the rest taxa were merged into Others.

The significant features of oral and gut microbiota in professional football players

The linear discriminant analysis (LDA) revealed statistically significant features that characterized oral and gut microbiota profiles in professional football players. Using the Kruskal–Wallis rank-sum test, the features at the species taxonomic level with the most important differential abundance between two groups of professional football players were revealed. In Fig. 9A and B, the most significant differential bacterial species in oral and gut profiles, respectively, were shown. The most important bacterial species discovered in oral and gut microbial profiles of professional football players (Table 1).

Fig. 9.

Linear discriminant analysis (LDA) scores of differentially abundant species among amateurs (red bars) and professional football players (blue bars). Figure (A) shows microbial biomarkers at the species taxonomic level in the oral cavity, and (B)—in the intestines. Species enriched in each group with an LDA score > 2 are considered.

Table 1.

The bacterial species that were statistically significant differentiated the oral and gut microbial profiles of professional and amateur football players.

Species of oral microbiota	p-value	Species of gut microbiota	p-value
Veillonella parvula	0.000	Neglecta timonensis	0.006
Actinomycenaeslundii	0.002	Streptococcus salivarius	0.007
Fusobacterium nucleatum	0.003	Eubacterium biforme	0.012
Fusobacterium canifelinum	0.003	Dialister succinatiphilus	0.015
Gemella morbillorum	0.004	Parabacteroides johnsonii	0.016
Haemophilusputorum	0.004	Phascolarctobacterium faecium	0.016
Actinomyceodontolyticus	0.007	Ruminococcus faecis	0.019
Actinomycegerencseriae	0.008	Lachnospiraceae bacterium	0.027
Streptococcuperoris	0.010	Bacteroides coprocola	0.046
Oribacterium asaccharolyticum	0.014
Campylobacter concisus	0.014
Capnocytophaga gingivalis	0.018
Rothia dentocariosa	0.018
Prevotella histicola	0.020
Actinomycegraevenitzii	0.020
Porphyromonaendodontalis	0.020
Capnocytophaga granulosa	0.040
Prevotella melaninogenica	0.042
Prevotella oris	0.042
Veillonella tobetsuensis	0.042
Streptococcusanguinis	0.042
Corynebacterium matruchotii	0.047
Dialister invisus	0.047
Tannerella forsythia	0.047
Selenomonanoxia	0.047

Linear discriminant analysis

The LEfSe algorithm was used for biomarker discovery (Fig. 10). Physical activity was related to different features at different taxonomic levels in the two groups. Figure 8 demonstrates the features with significant differential abundance regarding genus level. Physical activity was related to the increased abundance of Streptococcus salivarius, that was the most abundant species present in the gut microbiota of professional football players after intensive training (p < 0.003). The highest abundance among bacteria species before intensive physical activity was noticed. Before the intense training, the highest abundance of Barnesiella intestinihominis (p < 0.03) was detected in the gut microbiota.

Fig. 10.

The effect size of differentially abundant features was calculated with the Kruskal–Wallis rank-sum test based on the LEfSe algorithm. Only the features with significant differential abundance (p < 0.005) were shown. Group 0 represents the gut microbiota of professional football players before intensive training, whereas 1 represents the gut microbiota of professional football players after intensive training.

In the oral microbiota, the specific biomarkers were discovered at different taxonomic levels as shown in Fig. 11. After intensive physical activity, a high abundance of bacteria family Prevotellaceae, and Neisseriaceae were observed in professional football players Within them, the most abundant species were Prevotella melaninogenica (p < 0.000), Prevotella pallens (p < 0.000), Prevotella salivae (p < 0.000), Prevotella brevis (p < 0.000), Prevotella nanceiensis (p < 0.001), Prevotella scopus (p < 0.007). In Neisseriaceae, the highest abundance of Neisseria perflava (p < 0.002) was observed. After intensive physical activity, Rothia dentocariosa (p < 0.000), Rothia aeria (p < 0.000), Corynebacterium matruchotii (p < 0.001), Capnocytophaga leadbetteri (p < 0.002), Campylobacter gracilis (p < 0.003), Streptococcus sanguinis (p < 0.003), Dialister invisus (p < 0.003), Corynebacterium matruchotii (p < 0.004), were the most abundant species in the oral microbiota.

Fig. 11.

The effect size of differentially abundant features was calculated with the Kruskal–Wallis rank-sum test based on the LEfSe algorithm. Only the features with significant differential abundance (p < 0.005) were shown. Group 0 represents the oral microbiota of professional football players before intensive training, whereas 1 represents the oral microbiota of professional football players after intensive training.

Material and methods

Study design and subject characteristics

A prospective cohort study was conducted on 20 professional football players from one of the Polish football clubs and 12 amateurs. The mean age of professional football players was 26 ± 7 ages. In the amateurs, the mean age was 24 ± 6. All subjects of this group were of Polish ethnicity. Amateur players had limited intensity training during the league season, in contrast to professional football players. The amateurs trained twice a week for 1,5 h, and they participated once to twice a month in football matches. The professional football players trained every day for 4 h and they participated in one to two matches per week. In contrast to the amateurs, professional football players created a more homogeneous group due to a similar lifestyle, training intensity, and a similar diet created and controlled by a dietitian. However, the type of physical activity in both groups was similar, and all participants were matched with similar age, BMI, and health status. In two study groups, basic morphological parameters, including CRP were measured, and all were in the referential ranges.

For both groups, the same inclusion criteria were age between 18 and 40, active participation in group training, and informed consent to participate in the study. The exclusion criteria included: antibiotic treatment within two months of the sample collection procedure, serious injuries/breaks from training within two weeks of the sample collection procedure, and suffering from any immunological, gastrointestinal, or cardiovascular conditions.

Written informed consent was obtained from all study participants. The Ethical Committee of Jagiellonian University Medical College approved the protocol of the Jagiellonian University Medical College study. The study was conducted following the Declaration of Helsinki and adhered to Good Clinical Practice guidelines.

Samples collection

Stool samples and oral swabs were collected from both participant groups at a one-time point in November. This month was the highest physical activity for professional football players in the middle of the league season. The training load in this group was relatively constant over the year. Oral swabs and stool samples were collected from professional players again twice. The first collection was performed in the middle of the league season, the period of the highest physical activity. The second collection was performed at the end of 4 week-long intra-season breaks between the second half of December and the first half of February. This period between holidays and the next practical camp is characterized by low physical activity levels, with no preplanned training regimes. All professional football players were asked to collect fecal and oral samples before being well-instructed about the collection procedure. Feces were collected using a fecal collection kit (EasySampler, ALPCO, Salem) containing RNAlater (Sigma-Aldrich). The BactiSwabTM NPG Collection and Transport System (ThermoFisher Scientific, Waltham, MA, USA) were applied for oral swab collection. Both stool and oral samples were immediately transported on ice and stored at − 80 °C until further processing. Both fecal and swab samples were collected with the highest avoidance of contamination, which was critical for this microbiome study. The bacterial genomic DNA was extracted using commercially available assays, QIAamp BiOstic Bacteremia DNA Kit (QIAGEN, Hilden, Germany) for buccal swabs, and QIAamp PowerFecal Pro DNA Kit (QIAGEN, Hilden, Germany) for fecal samples, respectively. Then, the quantity and quality of bacterial genetic material were measured using spectrophotometer NanoDrop ND-1000 (Thermo Electron Corporation, West Palm Beach, FL, USA) and fluorometer Qubit 4 (Invitrogen, Waltham, MA, USA), respectively. The DNA samples were stored at − 20 °C until further analysis.

Genetic library construction

The gene-specific sequences targeting the V3 and V4 regions of the 16S rRNA gene were used to construct libraries. The specific primers were adapted from the Klindworth et al. publication⁸. When creating libraries, the Protocol for Preparing 16S Ribosomal RNA Gene Amplicon for Illumina MiSeq System (Part#15044223Rev.B.) from Illumina was adjusted. The KAPA HiFi HotStart Ready Mix (ROCHE, Basel, Switzerland) was used to perform the PCR-based amplification. All procedural steps were performed according to the manufacturer’s recommendations.

The amplification was performed under the following thermal profile 95 ◦C for 1 min, 55 ◦C for 1 min, then 72 ◦C for 1 min for 30 cycles, followed by a final extension at 72 ◦C for 5 min. Then, the amplicons were indexed with specific sequencing adapters following the Nextera XT Index Kit v2 from Illumina. The thermal conditions of indexing were as follows: 95 °C for 3 min, eight cycles of 95 °C for 30 s, 55 °C, 72 °C for 30 s, followed by a final extension at 72 °C for 5 min, and hold at 4 °C. Before sequencing, the amplicons were measured using Qubit 4.0 Fluorometer (Invitrogen) and Bioanalyzer (Agilent, Santa Clara, CA, USA) using Bioanalyzer DNA 1000 chips. The libraries with appropriate integrity and size, about 630 bp, were pooled in equimolar concentrations and then sequenced. The sequencing was performed on the MiSeq instrument (Illumina, San Diego, CA, USA) using a 300 × 2 V3 Kit and PhiX Control V3 from Illumina.

Statistical methods and analysis

The raw data, collected as the FASTQ files, were classified using the Illumina16S Metagenomics workflow. The classification was based on the algorithm with the high-performance implementation of the Ribosomal Database Project (RDP) classifier, described by Wang Q. et al. in 2007⁹. The open-reference operational taxonomic unit (OUT).

s were prepared based on these classifications. Then, a further detailed analysis was performed. The Greengenes database version 13.5 was used to conduct the taxonomic assignment of individual datasets¹⁰. Alpha and beta diversity were calculated using QIIME 2.0 software with Python scripts¹¹. Alpha diversity was calculated based on the sequence similarity at 97%. The richness was calculated as the amount of unique OTUs found in each sample and presented as observed OTUs. The count of unobserved species based on low-abundance OTUs was presented as ACE and Chao1 indices. Beta diversity (the distance and dissimilarities in-between microbial communities) was determined based on Jaccard, Bray–Curtis, and Jensen-Shannon Divergence indices calculated by QIIME. The distances were visualized by principal coordinate analysis (PCoA)¹². The clustering and statistical analysis were performed with LEfSe and the Microbiome Analyst platform¹³. The characteristic features of oral and intestinal microbiota profiles were determined using the linear discriminant analysis (LDA) effect size with LEfSe. The discovered microbial biomarkers with statistical significance and biological relevance were described based on the normalized relative abundance matrix, Kruskal–Wallisly’s rank-sum test, the significant alpha at 0.05, and the effect size threshold of 2. The hierarchical structure of taxonomic classifications was characterized using the median abundance and the non-parametric Wilcoxon Rank Sum test to show the taxonomic differences between the microbial communities and the abundance profiles of the experimental groups.

Discussion

We conducted a cohort study to assess the differences in the oral and gut microbiota composition between professional football players and amateurstraining. So far, studies have mainly focused on athletes who perform selectively aerobic (runners/cyclists) or anaerobic (sprinters) exercise. We chose football players for the study due to physical activity characterized by mixed intensity, consisting of both aerobic and anaerobic exercise. Compared to the previously published reports, the results from this study follow the results from studies on animals and adults, which suggested that exercise modified the gut microbiota through increased gut microbial diversity and the Firmicutes phylum, and an enrichment microbiota profile in butyrate-producing taxa such as Clostridiales, Roseburia,

Lachnospiraceae, Erysipelotrichaceae. The species dominated in the gut microbiota of professional football players belong mainly to the Clostridiales (Eubacterium biforme, Blautia wexlerae, Ruminococcus faecis, Neglecta timonensis, Ruminococcus lactaris)). Among bacteria species that dominated the gut microbiota of professional football players, there were also Streptococcus salivarius belonging to the Bacilli class of bacteria, Parabacteroides johnsonii (Bacteroidia), and Phascolarctobacterium faecium (Negativicutes).

Streptococcus salivarius is a predominant commensal inhabitant that has been shown to down-regulate nuclear transcription factor (NF-кB) in human intestinal cells, influencing a typical immunological response of the host mucosal immune system^14–16. Several species from Parabacteroides have also been associated with producing succinate and secondary bile acids^17,18. Phascolarctobacterium genus can produce SFCAs like acetate and propionate and Phascolarctobacterium species have been reported to be present in healthy and physically active subjects¹⁹. The high abundance of Phascolarctobacterium species was positively related to insulin sensitivity and secretion²⁰. Moreover, Wu et al. described that Phascolarctobacterium could also be associated with the metabolic state and mood of the host¹⁹.

We observed increased alpha diversity after the intensive physical activity, in the gut microbiota composition. Similar findings were reported in several rodent-based and human studies^5,21,22. Contrarily to gut microbiota results, the decrease of richness in the composition of microbial communities in the oral cavity, after intensive physical activity. The differences (beta diversity) between sample sets derived from the same site were also noted. While gut microbiota samples collected in two time-points were to a large degree comparable, oral microbiota samples significantly varied. These results indicate that microbiota located in different sites of the human body may respond differently to specific interventions such as increased physical activity, which as a topic is currently not well explored in scientific literature.

One report characterizes the gut microbial communities in female professional football players. In that study, the authors demonstrated that the gut microbiota was predominantly composed of bacteria from the phyla Firmicutes and Bacteroidetes. Among the genera, Faecalibacterium and Collinsella were the most abundant. They showed two species, Faecalibacterium prausnitzii and Collinsella aerofaciens which had the highest predominance²³. The oral microbial communities in professional football players were dominated mainly by Actinobacteria, Fusobacteria, Negativicutes, Bacilli, and Epsilonproteobacteria.

During the high activity period, oral microbiota composition was characteristic of a higher abundance of Bacteroidetes and Neisseriaceae and decreased abundance of Firmicutes, Cornyebacteriaceae, and Flavobacteria. Order and taxa analysis of gut microbiota samples collected during the low-activity period revealed an increased abundance of generally considered beneficial Lactobacillales and Streptococcus with a decreased abundance of Barnesiellaceae microbes. Bacteroides spp. Abundance is a typical finding for people consuming diets high in animal proteins and fats, such as athletes²⁴. An increased ratio of Firmicutes to Bacteroides is associated with an increased risk of obesity²⁵. It has been shown that some strains of bacteria from the genera Lactobacillus increase the absorption of minerals and vitamins, reduce lactose intolerance, have anti-diabetic properties, lower cholesterol levels, increase resistance to pathogen infections, and reduce the incidence of colon cancer. These bacteria have an anti-inflammatory effect on the local and systemic levels, improving the functioning of the immune system⁴.

This study has several limitations. While all players were advised to adhere to their everyday dietary regime, it is possible that their compliance was lower during the intra-seasonal break of professional football players. The gastrointestinal microbiota is involved in the body's homeostasis especially in the intestine by participating, for example, in the barrier function, extraction of nutrients from the diet, and conjugation of bile acids. Many reports show that gut microbiota changes depend on many individual factors, particularly in sportives, including energy expenditure, diet, and drug intake (especially antibiotics)^1,26–31. Since we did not assess differences in nutritional intake, detected alterations in microbiota may be partially caused by altered consumption of different nutrition products. Thus, this dissimilarity in microbiota may be partially caused by various diets. The research groups were small, and the individual diversity of the microbiota of different athletes could have distorted the study results. Additionally, study groups consisted only of men, making it impossible to demonstrate sex-related microbiota differences. Moreover, with professional football players characterized by the short duration of the low activity period, it is possible that some changes to the microbiota were not detected as they require more time to manifest. We should notice that the participants' hygiene habits were not under our control during the study. Similar to different environmental factors they also could influence the microbial profiles. We also did not perform, the analysis of biochemical parameters related to immunological and performance-related adaptations that could be linked with gut microbiota. Taking above-mentioned limitations, we suggest that future studies should consider microbial analysis taking into account other factors that strongly influence its homeostasis and functions.

Nevertheless, this study provides new insight into the effects of lifestyle and exercise on oral and gut microbiota composition. Despite some limitations, this study can serve as a starting point for large-scale studies comparing people engaging in one type of physical activity. Further research in this area will allow us to understand the impact of stress loads on microbiota and microbiota on the results achieved by professional athletes. Understanding the influence of microbiota on the body of professional athletes may allow for its modifications in the future to accelerate the regeneration of the body and increase the resistance of professional athletes to diseases by, for example, changing their diet or taking probiotics. Discoveries in sports medicine can be easily transferred to everyday medical practice.

Conclusions

This study is the first to compare two groups of male football players with different training loads, lifestyles on the professional and amateur levels, and intensity (before and after intensive football training) of training within a group of professional football players regarding the oral and gut microbiota composition. The result suggests that the professional practice of football impacts the diversity of composition of oral and, to a lesser degree, intestinal microbiota. The significant variability of microbiota and the multitude of factors that impact the composition of microorganisms composing microbiota force us to proceed with large-scale studies on the understanding role of microbiota in the human body.

Author contributions

Conceptualization B.Z., SU; methodology B.Z, and P.P..; software B.Z., validation B.Z.; formal analysis B.Z., P.P.; investigation B.Z, S.U.; resources B.Z.; data curation B.Z.,; writing—original draft preparation B.Z., SU, OC, JW; writing—review and editing B.Z., JW; visualization B.Z.; supervision B.Z.; project administration B.Z. All authors have read and agreed to the published version of the manuscript.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.