A Comparative Study of Selected Gut Bacteria Abundance and Fecal pH in Bodybuilders Eating High-Protein Diet and More Sedentary Controls
1
Department of Food and Nutrition, Poznan University of Physical Education, 61-871 Poznań, Poland
2
Department of Physical Education and Sports Science, School of Physical Education & Sport Sciences, Democritus University of Thrace University Campus, 69100 Komotini, Greece
*
Author to whom correspondence should be addressed.
Nutrients 2021, 13(11), 4093; https://doi.org/10.3390/nu13114093
Submission received: 15 October 2021
/
Revised: 9 November 2021
/
Accepted: 13 November 2021
/
Published: 16 November 2021
(This article belongs to the Special Issue Exercise, Microbiota, Health and Performance)
Abstract
:Bodybuilders tend to overeat their daily protein needs. The purpose of a high-protein diet is to support post-workout recovery and skeletal muscle growth; however, its exact impact on gut microbiota still remains under investigation. The aim of this study was to assess the differences in selected gut bacteria (Faecalibacterium prausnitzii, Akkermansia muciniphila, Bifidobacterium spp., and Bacteroides spp.) abundance and fecal pH between the group of amateur bodybuilders and more sedentary control group. In total, 26 young healthy men took part in the study, and their daily nutrients intake was measured using a dietary interview. Real-time PCR was used to assess the stool bacteria abundance. Both groups reported fiber intake within the recommended range, but bodybuilders consumed significantly more protein (33.6% ± 6.5% vs. 22% ± 6.3%) and less fat (27.6% ± 18.9% vs. 36.4% ± 10%) than controls. Study results showed no significant differences in terms of selected intestinal bacteria colony forming unit counts. Significantly higher fecal pH in the bodybuilders’ fecal samples was observed in comparison to the control group 6.9 ± 0.7 vs. 6.2 ± 0.7. Gut microbiota composition similarities could be a result of appropriate fiber intake in both groups.
1. Introduction
Bodybuilding aims to develop muscle mass, maintain symmetry, and keep the body fat levels as low as possible. To achieve these goals, bodybuilders use specific diets and resistance training plans. Building muscle mass requires caloric intake above the level of energy expenditure and it usually takes place in the off-season (after the contest). Another important element affecting skeletal muscle hypertrophy during this phase is the increased protein supply throughout the day. Bodybuilders should consume between 1.6 and 2.2 g of protein per every kilogram of the body mass (g/kg b.w.) [1]. Together with caloric surplus, it creates an anabolic environment for post-workout recovery and muscle protein synthesis (MPS) [2]. On the other hand, the pre-contest diet of bodybuilders is low in calories. Caloric deficit is necessary to activate catabolic pathways involved in loosing body fat mass. Contest preparation diets should provide recommended amounts of protein to avoid decrease in muscle mass. However, it has been reported that bodybuilders often exceed the recommended amounts of protein, as it reaches levels of 4.3 g/kg b.w./day (men) and 2.8 g/kg b.w./day (women) [3]. High-protein diets and protein supplements allow bodybuilders to increase muscle mass, but its effects on the gut microbiota needs further research.
The variety and number of health-promoting intestinal bacteria depend on such factors as the level of physical activity and diet quality. Altering protein or carbohydrates and dietary fiber intake in bodybuilders may change the gut bacteria abundance and composition and influence host metabolism and immune function [4]. Some of the gut bacteria are able to synthesize amino acids de novo and it affects the level of nitrogen in the body alongside with dietary protein intake. Inadequate fiber intake may enhance proteolytic fermentation due to decreased availability of fermentable carbohydrates. As a consequence, it may result in potentially harmful metabolites production increase [5]. Moreover, microbial fermentation of undigested peptides is another important source of molecules that contribute to the body’s amino acid pool. It also affects inflammation in the host’s tissues, which results in tissue permeability modulation [6].
Optimal composition of the gut microbiota is important for nitrogen balance and muscle protein synthesis, but also for muscle glycogen storage and oxidative stress management [7]. Intestinal bacteria promote carbohydrate fermentation and short-chain fatty acids (SCFAs) production. SCFAs (e.g., butyric acid, propionic acid, acetic acid) enhance the intestinal epithelial membrane, support absorption of electrolytes, and regulate glucose metabolism in skeletal muscles [8]. Gut microbiota imbalances (dysbiosis) may cause a number of gastrointestinal disorders, having a negative impact on sports results and overall health and wellbeing. It is a bidirectional connection in which exercise induces a health-promoting shift in the microbial environment that affect the host’s energy metabolism, oxidative stress balance, and immune system functioning. However, excessive training frequency and intensity may lead to negative changes within intestinal microbiota and limit muscle recovery and adaptations.
There are clear indications that sports training changes the gut microbiota. Nevertheless, in a human study, it is difficult to distinguish the effect of diet from that of exercise. We attempted to verify whether a high-protein diet used by bodybuilders modulates the abundance of health-promoting gut bacteria. The main objective of this study was to compare the targeted gut microbiota and stool pH between amateur bodybuilders on a high-protein diet and individuals who did not practice resistance training and consumed a balanced diet.
2. Materials and Methods
2.1. Participants
The study group consisted of 26 young healthy men aged 22–28 years. There were two subgroups: amateur bodybuilders (n = 11) and controls of a similar age (n = 15). Bodybuilders’ mean experience was 5 ± 3 years of training with a frequency of 5 trainings per week (total weekly training time: minimum of 7.5 h). Members of the sport club TKKF Winogrady took part in this study during the muscle-building phase of a contest preparation diet. The control group consisted of students of the Poznan University of Physical Education, who reported low or medium levels of physical activity and a balanced diet.
Before entering the study, participants were interviewed on their training, nutrition, and dietary supplementation history and habits. The study inclusion criteria were as follows: voluntary written consent, age of 18 or older, good overall health, no gastrointestinal or respiratory infections in the last 4 weeks, and no injuries followed by inflammation in the last 4 weeks. We excluded from the study participants who used antibiotics, proton pump inhibitors (PPIs), probiotics, prebiotics, androgenic-anabolic steroids, oral antimicrobial agents, or travelled to countries with a different climate and habitual diet during the last 4 weeks before the study. We also excluded subjects who underwent hospitalization in the previous month. Information regarding health status, medical history, and total exercise workload was assessed using a questionnaire on the day of the survey.
The study design met the criteria of Ethics Guidelines of the Declaration of Helsinki. It was approved by the Local Ethics Committee at the Poznan University of Medical Sciences, reference numbers no.173/16. Only subjects who signed the consent could take part in the study. Data collection was conducted according to the Helsinki declaration for biomedical research on human subjects.
Daily intake of energy and nutrients was assessed using dietary interviews, based on participants’ three day nutrition (2 working days and 1 weekend day). Study subjects self-reported the estimated meal composition and weight of the used products in the diary. The amount of nutrients in participants’ diets was evaluated using the NUVERO application (Poland). An information on dietary supplements consumption (e.g., whey protein concentrate) was also recorded.
Dual-Energy X-ray Absorptiometry (DXA; GE Healthcare Lunar Prodigy Advance; GE Medical Systems, Milan, Italy) was used to perform an analysis of participants’ body composition. The subjects from both groups rested for over 24 h before taking part in the study. Before the examination started, subjects received clear instructions on the procedure rules and order.
2.2. Stool Sample Collection
Selected gut bacteria abundance analysis and fecal pH measurement required stool sample collection. The subjects were asked to bring it to the laboratory as quickly as possible (in under 24 h). The KyberKompaktPRO (Institute of Microecology) protocol was used to properly take the samples (3/4 of volume of 150-mL container, pieces of stool from up to 8 locations).
A QIAamp Fast DNA Stool Mini Kit (QIAGEN) was used to prepare selected gut bacteria DNA from feces. This procedure was conducted following the manufacturer’s instructions. The samples were frozen and left in the freezer until further analysis. Real-Time PCR (ABI 7300; ThermoFisher Scientific, Waltham, MA, USA) was used to perform anaerobic gut bacteria abundance analysis. Table 1 shows the selected primers (ThermoFisher Scientific) needed to assess the counts of Faecalibacterium prausnitzi, Akkermansia muciniphila of the genus Akkermansia, Bifidobacterium spp. of the genus Actinobacteria, and Bacteroides spp. of the genus Bacteroidetes.
The real-time PCR results were recalculated to bacteria count per gram. The amplification efficacy in all assays was higher than 90%. The standard curve showed a linear range across at least 5 logs of DNA concentrations with a correlation coefficient >0.99. The lowest detection limits of all assays were as low as 10–100 copies of specific bacterial 16S rDNA per reaction, which corresponds to 104–105 copies per gram of wet-weight feces. Knowing the values of the standards and their C(t) (cycle threshold), the obtained data were converted using the right coefficients. The standards used in the study are listed in Table 2.
The lowest value for bacteria detectability was 102 colony forming units (CFU) per gram of feces. To simplify the calculation, any results under this level were set as “0” in the statistical analysis. After the conversion of microbiota analysis results, it was shown as the decimal logarithm (Log10). The procedures of the gut bacteria real-time PCR analysis were performed according to the instructions given by Institute of Microecology in Herborn, Germany. Table 3 shows the reference values for the selected bacteria.
2.3. Statistical Analysis
The obtained test results were examined using statistic tools to show the differences between observed groups (STATISTICA 13.0; StatSoft Inc., Palo Alto, CA, USA). The results presentation includes mean values with standard deviations (± SD) and/or medians (Me) with Q1 and Q3 quartiles. Since the obtained data violated normality and demonstrated heterogeneous variability, non-parametric tests were used. The significance of differences between the bodybuilder and control group outcomes was assessed using the Mann–Whitney test.
3. Results
3.1. Body Composition Analysis
The performed analysis demonstrated a significant difference in body mass (p < 0.05) and fat-free mass (p < 0.05) between the groups (Table 4).
3.2. Nutrients Intake Analysis
The dietary survey analysis presented significant differences in protein (p < 0.05) and fat (p < 0.01) consumption between the examined groups (Table 5). Figure 1 shows the proportions of nutrients consumed by the study participants. Bodybuilders ate less calories from fat and more calories from protein than controls. However, there was no significant difference in protein consumption expressed as g/kg b.w. The mean fiber intake in both groups reached the levels recommended for daily consumption.
3.3. Stool Samples Analysis
The performed analysis showed no statistically significant differences in the examined gut bacteria abundance expressed as decimal logarithm of CFU per gram of feces. This study revealed a significant difference in fecal pH values (p < 0.05) between bodybuilders and the control group (Table 6).
4. Discussion
This study’s results showed that bodybuilders’ high-protein diet with fiber intake on the recommended level, in comparison to a standard diet, did not promote any significant changes in the abundance of the examined health-promoting gut bacteria. However, the bodybuilders’ fecal pH was higher than that of the control group. The results of studies carried out by other researchers showed that long-term dietary changes have a big impact on the dominant gut bacteria species, while changes of shorter duration play only a temporary role [9,10]. However, there is still insufficient information on this issue, and it is limited to people with protein intake that exceed the nutritional requirements by 2–4 fold.
Our study was performed during the mass-gaining (muscle-building) phase of the bodybuilders’ diet. During this period, bodybuilders focus on muscular hypertrophy and it often leads to an increase in adipose tissue deposition. Therefore, there were notable differences in total body mass (mean ± SD: 96.4 ± 8.9 vs. 83.4 ± 13.2; p < 0.005) and fat-free body mass (mean ± SD: 80.6 ± 8.9 vs. 69.7 ± 6.4; p < 0.005) between bodybuilders and the control group (Table 4). The systematic review of 18 manuscripts on bodybuilding showed that the mean percentage of adipose tissue in this group of athletes reaches 12.1 ± 2.5% in the off-season, while contest preparation requires a body-fat level of about 6% (for men) [3]. In our study, bodybuilders’ mean percentage of body fat mass was 14% and did not differ significantly from the control group men (Table 4).
The analysis performed in our study showed that male bodybuilders consumed on average 3516 kcal per day, with 38.8% of that energy coming from carbohydrates, 33.6% from protein, and 27.6% from fats. We found no significant difference in dietary intake between the bodybuilders and control group men in calorie intake per day, percentage of carbohydrates in energy intake, and fiber intake (Table 5). Bodybuilders’ protein intake (%) was significantly higher than that of the control group (Mean ± SD: 33.6% ± 6.5% vs. 22% ± 6.3%; p < 0.05) and exceeded the recommended 25–30% of daily energy intake from protein [11]. Still, the mean protein consumption, expressed as g/kg b.w., in the bodybuilder group fitted the recommended ranges and was not as high as expected and did not differ significantly from that of the control group (mean ± SD: 2.1 ± 1.5 vs. 1.7 ± 1.0%; p < 0.39). A protein intake level that is adjusted to the body’s needs is essential for maximizing muscle adaptations, increasing muscular hypertrophy, and gaining strength [12]. Bodybuilders consuming a high-protein diet focus on the sources of branched-chain amino acids, especially leucine (e.g., whey, lean meat, eggs). Together with its metabolite, β-hydroxy-β-methylbutyrate (HMB), it may affect the gut environment and nutrient metabolism [13,14]. On the other hand, excessive protein intake increases the abundance of protein-fermenting bacteria, such as Clostridium, Bacillus, Staphylococcus, and other species of the Proteobacteria family [15]. There are indications from animal studies that increasing the ratio of energy intake from protein to that from carbohydrates and fats may affect microbial composition and protein fermentation as well as its toxic product levels [16]. Moreover, a protein-enriched diet may cause a reduction of carbohydrate-fermenting bacteria, such as Bacteroides, Lactobacillus, Bifidobacterium, Prevotella, Ruminococcus, Roseburia, and Faecalibacterium [17]. The metabolic processes following the fermentation of incompletely digested protein in the colon may lead to toxic metabolite production (e.g., ammonia, biogenic amines, indole compounds, and phenols). These factors are known enhancers of inflammation and tissue permeability [18]. It is a potential cause of intestine tissue damage and metabolic, immune, and neurological disruptions.
Although increased protein intake was observed in bodybuilders in the study, there was no significant difference in the abundance of selected beneficial bacteria (Table 6). Although the control group consumed more fiber, both tested groups met the criteria for recommended fiber intake. High protein intake’s effects on the microbiome could have been attenuated by appropriate carbohydrate and fiber intake [7]. It stands in accordance with the conclusions of other authors saying that there is no significant difference in gut microbiota diversity between bodybuilders who consume a high-protein/low-fiber diet and the general population [19]. However, the authors of this paper emphasize that the relationship between diet, exercise, and gut microbiota is still very poorly explained.
The pH along the human colon usually varies from 5 to 7, and it depends on fermentation processes, secretion of bicarbonate by colonic epithelial cells, and absorption of microbial metabolites by host epithelial cells [20]. In this conducted study, significantly higher fecal pH levels (mean ± SD: 6.9 ± 0.7 vs. 6.2 ± 0.7; p < 0.05) were observed in bodybuilders compared to the male control group (Table 6). High levels of animal-based dietary protein intake may play a role in increasing fecal pH values in bodybuilders, as proteolytic putrefactive bacteria are able to produce alkaline metabolites [21,22]. There are indications that altered pH in the gut may change the microbiota composition and its metabolism (e.g., production of short-chain fatty acids), but in our study, there were no statistically significant differences between the groups in the tested fecal bacteria abundance. In both groups, a reduced abundance of bacteria, such as Bifidobacterium spp., Faecalibacterium prausnitzii spp., and Akkermansia muciniphila spp., was noted (Table 6). Our observations seem to confirm the findings of other authors showing an increasing deficit of F. prausnitzii and A. municiniphia in the human intestine over the past few years [23]. It also confirms other authors’ findings saying that the gut abundance of Bacteroidetes spp. is generally not affected by higher protein intake [16,24].
Author Contributions
Conceptualization, J.K.; Methodology, J.K.; Formal Analysis, J.K.; Investigation, J.K. and J.S.; Resources, J.K.; Samples collection, J.S.; Data Curation, J.K. and J.S.; Writing—Original Draft Preparation, J.K. and J.W.; Writing—Review and Editing, K.L. and J.W.; Supervision, J.K.; Project Administration, J.K.; Funding Acquisition, J.S. and J.W. All authors have read and agreed to the published version of the manuscript.
Funding
This study was partly funded by Poznan University of Physical Education in a “Development of young researchers” program.
Institutional Review Board Statement
The study protocol was written in accordance with the Ethics Guidelines of the Declaration of Helsinki. It was approved by the Local Ethics Committee at the Poznan University of Medical Sciences, reference numbers no.173/16.
Informed Consent Statement
Written informed consent for publication of their tests details was obtained from the study participants. A copy of the consent form is available for review by the Editor of this journal.
Data Availability Statement
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
Acknowledgments
We would like to thank Mirosława Gałęcka from Institute of Microecology, Poznań, Poland for her excellent technical support during fecal samples analysis.
Conflicts of Interest
The authors declare no competing interest.
References
- Iraki, J.; Fitschen, P.; Espinar, S.; Helms, E. Nutrition Recommendations for Bodybuilders in the Off-Season: A Narrative Review. Sports 2019, 7, 154. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rozenek, R.; Ward, P.; Long, S.; Garhammer, J. Effects of high-calorie supplements on body composition and muscular strength following resistance training. J. Sports Med. Phys. Fit. 2002, 42, 340–347. [Google Scholar]
- Spendlove, J.; Mitchell, L.; Gifford, J.; Hackett, D.; Slater, G.; Cobley, S.; O’Connor, H. Dietary Intake of Competitive Bodybuilders. Sports Med. 2015, 45, 1041–1063. [Google Scholar] [CrossRef] [PubMed]
- Korpela, K. Diet, Microbiota, and Metabolic Health: Trade-Off Between Saccharolytic and Proteolytic Fermentation. Annu. Rev. Food Sci. Technol. 2018, 9, 65–84. [Google Scholar] [CrossRef] [PubMed]
- Neis, E.P.; Dejong, C.H.; Rensen, S.S. The role of microbial amino acid metabolism in host metabolism. Nutrients 2015, 7, 2930–2946. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Diether, N.E.; Willing, B.P. Microbial Fermentation of Dietary Protein: An Important Factor in Diet-Microbe-Host Interaction. Microorganisms 2019, 7, 19. [Google Scholar] [CrossRef] [Green Version]
- Przewłócka, K.; Folwarski, M.; Kaźmierczak-Siedlecka, K.; Skonieczna-Żydecka, K.; Kaczor, J.J. Gut-Muscle AxisExists and May Affect Skeletal Muscle Adaptation to Training. Nutrients 2020, 12, 1451. [Google Scholar] [CrossRef]
- Barton, W.; Penney, N.C.; Cronin, O.; Garcia-Perez, I.; Molloy, M.G.; Holmes, E.; Shanahan, F.; Cotter, P.D.; O’Sullivan, O. The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level. Gut 2018, 67, 625–633. [Google Scholar] [CrossRef] [PubMed]
- Bolte, L.A.; Vich Vila, A.; Imhann, F.; Collij, V.; Gacesa, R.; Peters, V.; Wijmenga, C.; Kurilshikov, A.; Campmans-Kuijpers, M.J.E.; Fu, J.; et al. Long-term dietary patterns are associated with pro-inflammatory and anti-inflammatory features of the gut microbiome. Gut 2021, 70, 1287–1298. [Google Scholar] [CrossRef] [PubMed]
- Singh, R.K.; Chang, H.W.; Yan, D.I.; Lee, K.M.; Ucmak, D.; Wong, K.; Abrouk, M.; Farahnik, B.; Nakamura, M.; Zhu, T.H.; et al. Influence of diet on the gut microbiome and implications for human health. J. Transl. Med. 2017, 15, 73. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lambert, C.P.; Frank, L.L.; Evans, W.J.; Lambert, D.C.P. Macronutrient Considerations for the Sport of Bodybuilding. Sports Med. 2004, 34, 317–327. [Google Scholar] [CrossRef]
- Stark, M.; Lukaszuk, J.; Prawitz, A.; Salacinski, A. Protein timing and its effects on muscular hypertrophy and strength in individuals engaged in weight-training. J. Int. Soc. Sports Nutr. 2012, 9, 54. [Google Scholar] [CrossRef] [Green Version]
- Duan, Y.; Zhong, Y.; Xiao, H.; Zheng, C.; Song, B.; Wang, W.; Guo, Q.; Li, Y.; Han, H.; Gao, J.; et al. Gut microbiota mediates the protective effects of dietary β-hydroxy-β-methylbutyrate (HMB) against obesity induced by high-fat diets. FASEB J. 2019, 33, 10019–10033. [Google Scholar] [CrossRef] [PubMed]
- Zhang, L.; Li, F.; Guo, Q.; Duan, Y.; Wang, W.; Zhong, Y.; Yang, Y.; Yin, Y. Leucine Supplementation: A Novel Strategy for Modulating Lipid Metabolism and Energy Homeostasis. Nutrients 2020, 12, 1299. [Google Scholar] [CrossRef]
- Rinninella, E.; Raoul, P.; Cintoni, M.; Franceschi, F.; Miggiano, G.A.D.; Gasbarrini, A.; Mele, M.C. What is the Healthy Gut Microbiota Composition? A Changing Ecosystem across Age, Environment, Diet, and Diseases. Microorganisms 2019, 7, 14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhang, H.; Wielen, N.V.; Hee, B.V.; Wang, J.; Hendriks, W.; Gilbert, M. Impact of Fermentable Protein, by Feeding High Protein Diets, on Microbial Composition, Microbial Catabolic Activity, Gut Health and beyond in Pigs. Microorganisms 2020, 8, 1735. [Google Scholar] [CrossRef] [PubMed]
- Lin, R.; Liu, W.; Piao, M.; Zhu, H. A review of the relationship between the gut microbiota and amino acid metabolism. Amino Acids 2017, 49, 2083–2090. [Google Scholar] [CrossRef] [PubMed]
- Moya, A.; Ferrer, M. Functional Redundancy-Induced Stability of Gut Microbiota Subjected to Disturbance. Trends Microbiol. 2016, 24, 402–413. [Google Scholar] [CrossRef]
- Son, J.; Jang, L.G.; Kim, B.Y.; Lee, S.; Park, H. The Effect of Athletes’ Probiotic Intake May Depend on Protein and Dietary Fiber Intake. Nutrients 2020, 12, 2947. [Google Scholar] [CrossRef]
- Ilhan, Z.E.; Marcus, A.K.; Kang, D.W.; Rittmann, B.E.; Krajmalnik-Brown, R. pH-Mediated Microbial and Metabolic Interactions in Fecal Enrichment Cultures. mSphere 2017, 2, e00047-17. [Google Scholar] [CrossRef] [Green Version]
- David, L.A.; Maurice, C.F.; Carmody, R.N.; Gootenberg, D.B.; Button, J.E.; Wolfe, B.E.; Ling, A.V.; Devlin, A.S.; Varma, Y.; Fischbach, M.A.; et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 2014, 505, 559–635. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Trefflich, I.; Dietrich, S.; Braune, A.; Abraham, K.; Weikert, C. Short- and Branched-Chain Fatty Acids as Fecal Markers for Microbiota Activity in Vegans and Omnivores. Nutrients 2021, 13, 1808. [Google Scholar] [CrossRef] [PubMed]
- Ouyang, J.; Lin, J.; Isnard, S.; Fombuena, B.; Peng, X.; Marette, A.; Routy, B.; Messaoudene, M.; Chen, Y.; Routy, J.P. The Bacterium Akkermansia muciniphila: A Sentinel for Gut Permeability and Its Relevance to HIV-Related Inflammation. Front. Immunol. 2020, 11, 645. [Google Scholar] [CrossRef] [PubMed]
- Beaumont, M.; Portune, K.J.; Steuer, N.; Lan, A.; Cerrudo, V.; Audebert, M.; Dumont, F.; Mancano, G.; Khodorova, N.; Andriamihaja, M.; et al. Quantity and source of dietary protein influence metabolite production by gut microbiota and rectal mucosa gene expression: A randomized, parallel, double-blind trial in overweight humans. Am. J. Clin. Nutr. 2017, 106, 1005–1019. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Figure 1.
Comparison of daily nutrient intake of bodybuilders and controls.
Table 1.
Primers used for the determination of different bacteria.
| Name | Product Description | Sequence |
|---|---|---|
| Praus-F480 | Faecalibacterium prausnitzii forward starter | CAGCAGCCGCGGTAAA |
| Praus-R631 | Faecalibacterium prausnitzii reverse starter | CTACCTCTGCACTACTCAAGAAA |
| Akk.muc-F | Akkermansia muciniphila starter forward | CAGCACGTGAAGGTGGGGAC |
| Akk.muc-R | Akkermansia muciniphila starter reverse | CCTTGCGGTTGGCTTCAGAT |
| F-Bifid09c | Bifidobacterium spp. forward starter | CGGGTGAGTAATGCGTGACC |
| R-Bifid06 | Bifidobacterium spp. reverse starter | TGATAGGACGCGACCCCA |
| Bacter11 | Bacteroides spp. forward starter | CCTWCGATGGATAGGGGTT |
| Bacter08 | Bacteroides spp. starter reverse | CACGCTACTTGGCTGGTTCAG |
| Uni-F340 | Universal forward starter | ACTCCTACGGGAGGCAGCAGT |
| Uni-R514 | Universal reverse starter | ATTACCGCGGCTGCTGGC |
Table 2.
Standards applied for the determination of different microorganisms.
| Name | Among of DNA (Copies/mL) | Product Description |
|---|---|---|
| Bifidobacterium infantis DNA | 5 × 10⁸ | Standard in identification of Bifidobacterium spp., isolated from Bifidobacterium infantis |
| Bacteroides fragilis DNA | 2 × 10⁹ | Standard in identification of Bacteroides spp., isolated from Bacteroides fragilis |
| Faecalibacterium prausnitzii DNA | 7.8 × 10⁸ | Standard in identification of Faecalibacterium prausnitzii |
| Akkermansia muciniphila DNA | 3.9 × 10⁸ | Standard in identification of Akkermansia muciniphila |
Table 3.
Reference values for selected bacteria.
| Species [Genus] | Standard (Log10 CFU/g Feces) | Method |
|---|---|---|
| Bifidobacterium spp. | ≥8 | Real-time PCR |
| Bacteroides spp. | ≥9 | Real-time PCR |
| Faecalibacterium prausnitzii | ≥9 | Real-time PCR |
| Akkermansia muciniphila | ≥8 | Real-time PCR |
Table 4.
Comparison of basic characteristics of body composition.
| Bodybuilder Group (n = 11) | Control Group (n = 15) | Mann- Whitney (p-Value) | |||
|---|---|---|---|---|---|
| Mean ± SD | Median Q1 ÷ Q3 | Mean ± SD | Median Q1 ÷ Q3 | ||
| Age (years) | 27 ± 6 | 25 23 ÷ 28 | 29 ± 8 | 24 22 ÷ 37 | NS |
| Height [cm] | 182.0 ± 6.3 | 181.5 179.3 ÷ 185 | 181.7 ± 4.4 | 182 179 ÷ 185 | NS |
| Body mass [kg] | 96.4 ± 8.9 | 96.8 93.8 ÷ 103 | 83.4 ± 13.2 | 76.6 72.4 ÷ 99.8 | 0.0023 * |
| Body fat mass [%] | 14.0 ± 4.5 | 14.6 9.5÷18.2 | 15.3 ± 7.7 | 15.8 6.6 ÷ 20.7 | NS |
| Body fat mass [kg] | 13.2 ± 4.2 | 13.7 7.9 ÷ 16.7 | 13.5 ± 8.5 | 11.6 5 ÷ 21.1 | NS |
| Fat-free mass [kg] | 80.6 ± 8.9 | 81.1 74 ÷ 87.2 | 69.7 ± 6.4 | 70.6 63.2 ÷ 74.0 | 0.0035 * |
SD—standard deviation; Q1—lower quartile; Q3—upper quartile; * p < 0.005—Statistical significance; NS—no significant differences.
Table 5.
Comparison of daily intake of energy [kcal], protein [%; g/kg b.w.], carbohydrates [%], fats [%], and fiber [g] of bodybuilders and the control group.
Table 5.
Comparison of daily intake of energy [kcal], protein [%; g/kg b.w.], carbohydrates [%], fats [%], and fiber [g] of bodybuilders and the control group.
| Bodybuilders (n = 11) | Control Group (n = 15) | Mann-Whitney (p-Value) | |||
|---|---|---|---|---|---|
| Mean ± SD | Median Q1 ÷ Q3 | Mean ± SD | Median Q1 ÷ Q3 | ||
| Energy [kcal] | 3516 ± 1433 | 3032 2685 ÷ 3951 | 2882 ± 1422 | 2640 2038 ÷ 3233 | NS |
| Protein [%] | 33.6 ± 6.5 | 34.3 29.2 ÷ 39.2 | 22 ± 6.3 | 21.4 18.0 ÷ 24.0 | 0.0493 * |
| Protein [g/kg b.w.] | 2.1 ± 1.5 | 2.4 0.0 ÷ 3.1 | 1.7 ± 1.0 | 1.8 0.7 ÷ 2.4 | NS |
| Carbohydrates [%] | 38.8 ± 14.8 | 43.2 38.3÷45.6 | 44.7 ± 14.2 | 41.7 36.9 ÷ 48.0 | NS |
| Fat [%] | 27.6 ± 18.9 | 21.1 16.0 ÷ 27.4 | 40.4 ± 10.0 | 36.4 35.3 ÷ 41.6 | 0.0002 ** |
| Fiber [g] | 29.4 ± 11.8 | 26.7 25.0 ÷ 33.0 | 33.8 ± 24.9 | 31.6 15.5 ÷ 41.7 | NS |
SD—standard deviation; Q1—lower quartile; Q3—upper quartile; * p < 0.05; ** p < 0.001—Statistical significance; NS—no significant differences; g/kg b.w.—grams per kilogram of body weight.
Table 6.
Comparison of the targeted stool bacteria and fecal pH values of bodybuilders and the control group.
Table 6.
Comparison of the targeted stool bacteria and fecal pH values of bodybuilders and the control group.
| Bodybuilders (n = 11) | Control Group (n = 15) | Mann-Whitney (p-Value) | |||
|---|---|---|---|---|---|
| Mean ± SD | Median Q1 ÷ Q3 | Mean ± SD | Median Q1 ÷ Q3 | ||
| Bifidocacterium spp. | 6.4 ±0.4 | 6.3 8.9 ÷ 9.3 | 7.0 ± 0.6 | 7.0 8.3 ÷ 9.3 | NS |
| Bacteroides spp. | 9.0 ± 0.4 | 9.0 8.9 ÷ 9.3 | 8.8 ± 0.6 | 8.8 8.3 ÷ 9.3 | NS |
| F. prausnitzii | 8.3 ± 0.6 | 8.5 8.0 ÷ 8.7 | 7.9 ± 0.6 | 7.9 7.3 ÷ 8.3 | NS |
| A. Muciniphila | 6.0 ± 1.4 | 6.0 4.8 ÷ 7.3 | 5.6 ± 2.1 | 6.6 4.7 ÷ 7.0 | NS |
| Fecal pH | 6.9 ± 0.7 | 7.0 6.0 ÷ 7.5 | 6.2 ± 0.7 | 6.5 5.5 ÷ 6.5 | 0.0322 * |
SD—standard deviation; Q1—lower quartile; Q3—upper quartile; * p < 0.05—Statistical significance; NS—no significant differences.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Szurkowska, J.; Wiącek, J.; Laparidis, K.; Karolkiewicz, J. A Comparative Study of Selected Gut Bacteria Abundance and Fecal pH in Bodybuilders Eating High-Protein Diet and More Sedentary Controls. Nutrients 2021, 13, 4093. https://doi.org/10.3390/nu13114093
AMA Style
Szurkowska J, Wiącek J, Laparidis K, Karolkiewicz J. A Comparative Study of Selected Gut Bacteria Abundance and Fecal pH in Bodybuilders Eating High-Protein Diet and More Sedentary Controls. Nutrients. 2021; 13(11):4093. https://doi.org/10.3390/nu13114093
Chicago/Turabian StyleSzurkowska, Joanna, Jakub Wiącek, Konstantinos Laparidis, and Joanna Karolkiewicz. 2021. "A Comparative Study of Selected Gut Bacteria Abundance and Fecal pH in Bodybuilders Eating High-Protein Diet and More Sedentary Controls" Nutrients 13, no. 11: 4093. https://doi.org/10.3390/nu13114093
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
