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Nutrients 2017, 9(8), 792; https://doi.org/10.3390/nu9080792

Article
The Association between Cardiorespiratory Fitness and Gut Microbiota Composition in Premenopausal Women
by 1,2, 2,3,*, 2,4,5,6, 2,4, 1,2, 1,2, 1,2, 7, 8 and 1,2,3,4,*
1
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
2
Exercise, Health and Technology center, Shanghai Jiao Tong University, Shanghai 200240, China
3
Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Centre for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
4
Faculty of Sport and Health Sciences, University of Jyvaskyla, 40014 Jyvaskyla, Finland
5
Center for Life Course Epidemiology, Faculty of Medicine, University of Oulu, 90014 Oulu, Finland
6
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, SW7 2AZ London, UK
7
State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
8
Department of Medical Microbiology and Immunology, University of Turku, 20014 Turku, Finland
*
Authors to whom correspondence should be addressed.
Received: 12 May 2017 / Accepted: 19 July 2017 / Published: 25 July 2017

Abstract

:
The aim of this study was to investigate the association between cardiorespiratory fitness and gut microbiota composition in premenopausal women. The participants consisted of 71 premenopausal Finnish women (aged 19–49 years). Gut microbiota were analyzed using flow cytometry, 16S rRNA gene hybridization and DNA-staining. Maximum oxygen uptake (VO2max) was assessed by respiratory gas analyzer and body composition by Bioimpdance. We found that participants with low VO2max had lower Bacteroides, but higher Eubacterium rectale-Clostridium coccoides than the high VO2max group (p < 0.05 for all). VO2max was inversely associated with EreC (r = −0.309, p = 0.01) but not with other bacteria. VO2max also negatively correlated with fat% (r = −0.755, p < 0.001), triglycerides (r = −0.274, p = 0.021) and leptin (r = −0.574, p < 0.001). By contrast, EreC was positively associated with fat% (r = 0.382, p = 0.002), dietary fat intake (r = 0.258, p = 0.034), triglycerides (r = 0.390, p = 0.002) and leptin (r = 0.424, p = 0.001), but negatively with carbohydrate intake (r = −0.252, p = 0.034) and HDL (r = −0.26, p = 0.028). After adjusting for age and dietary intake, all the significant associations remained. However, after adjusting for fat%, the associations between VO2max and EreC disappeared. Our results suggest that cardiorespiratory fitness is associated with gut microbiota composition, independent of age and carbohydrate or fat intake. The association between VO2max and EreC, however, appears to be mediated by body fatness.
Keywords:
exercise; VO2max; gut microbiota; body fatness

1. Introduction

There is growing awareness that microbial communities colonize different regions of the gastrointestinal tract, playing a major role in the health and disease of their host [1]. In the healthy state, the commensal microbes help to digest and absorb nutrients, modulate the immune system and provide protection against enteropathogens [1,2]. Gut microbial imbalance, followed by a state of dysbiosis, in turn, is associated with obesity [3], type 2 diabetes [4], cardiovascular disease [5] and non-alcoholic fatty liver disease [6]. Modulation of the gut microbiome has therefore become a topic of considerable interest.
Although using antibiotics is considered to be an efficient way to modify the microbiota, recent studies have suggested that dietary modification and regular exercise may offer cost-effective alternative means to achieve the same end [7,8]. A cross-sectional study of professional rugby players showed that exercise is associated with gut microbial diversity, and that proportions of several microbial taxa were significantly higher in the rugby players compared with the control group [9]. A recent study by Estaki et al. [10] also showed a positive correlation between cardiorespiratory fitness and microbial diversity in a small group of young healthy adults. However, earlier studies were done in athletic men and young adults; a current study has shown that estrogen in women influences gut microbiota [11], but overweight or obese women with relatively low physical fitness remain to be studied.
Physical fitness is considered one of the most objective measures of the level of physical activity [12]. Cardiorespiratory fitness is the overall capacity of the cardiovascular and respiratory systems and the ability to carry out prolonged strenuous exercise. The maximal oxygen consumption (VO2max) attained during a graded maximal exercise to voluntary exhaustion has long been considered by the World Health Organization as the single best indicator of cardiorespiratory fitness [13]. Therefore, to study whether the level of physical fitness is associated with certain compositions of the gut microbiota, using VO2max as a segregate variable is possible option in a cross-sectional study design. The aim of the present study was to examine the relationship between gut microbiota and aerobic fitness in premenopausal women with diverse body composition (mostly overweight or obese with a sedentary lifestyle). More specifically, we examined (1) the correlations between cardiorespiratory fitness and specific bacteria groups, and (2) whether these correlations between cardiorespiratory fitness and specific bacteria groups were independent of age, dietary intake and whole-body fat mass.

2. Materials and Methods

2.1. Study Design and Subjects

The study participants consisted of 71 Finnish women (aged 19 to 49 years) who resided in the city of Jyvaskyla, Central Finland, which has a population of approximately 150,000 [14]. In our early study, 80% of the people who participated in our study were still living in Jyvaskyla after 10 years and there were no differences between people who were living inside the city compared to those who were living outside the city (unpublished data). However, duration of living in the city of Jyvaskyla was not a required inclusion criterion. The majority (80%) of the study participants were either overweight or obese; but had no diagnosed cardiovascular disease, type I or type II diabetes or serious musculoskeletal problems. None of the participants had been on antibiotics treatment during the past three months. The study protocol was approved by the ethical committee of the Central Finland Health Care District (No: 7/2011). Informed consent was obtained from each participant prior to the assessments.

2.2. Background Information

Background information, including medical history and current health status, was collected via self-administered questionnaires. The level of physical activity in terms of duration (exercise hours per week) and frequency (exercise times per week) was also collected by validated questionnaires [15,16]. Food consumption and intakes of total energy and energy-yielding nutrients were assessed from food records that were kept for three days (including two weekdays and one weekend day). Food diary records were analyzed for total calorie, protein, carbohydrate, and fat intake by using the software Micro-Nutrica (v3.1), developed by the Social Insurance Institution of Finland and updated with a database for new foodstuffs by the study nutritionist [17]. To minimize possible under-reporting, the proportion of total energy intake of energy-yielding nutrients (E%) was calculated and reported in this study.

2.3. Anthropometrical and Body Composition Assessments

Body height (cm) was measured using standardized protocols (a wall-fixed measuring device). Body weight (kg) and whole-body fat mass were assessed with light clothes and without shoes using bio-impedance (Inbody 720, Biospace Co. Ltd., Seoul, South Korea). Body mass index (BMI) was calculated according to weight/height2 (kg·m−2). Percent of body fat (fat%) was calculated as whole-body fat mass divided by weight.

2.4. Fitness Test

The maximum oxygen uptake (VO2max in mL/kg/min) was assessed by a bicycle ergometer under the supervision of a physician. The test began with a 2-min warm-up at 50 W. After that the intensity was increased by 25 W at 2-min intervals until exhaustion. Electrocardiography was monitored continuously and heart rate and maximal work load were recorded at the end of every load. Oxygen uptake was assessed by the breath-by-breath method using a respiratory gas analyzer (Sensor Medics Vmax, Yorba Linda, CA, USA). Maximal oxygen uptake was reached when the measured VO2 reached a plateau or started to decrease, or the subject felt she had reached her maximal level and wanted to stop the test. On the basis of the VO2max values, participants were divided into three groups by tertiles (low, moderate and high).

2.5. Blood and Biochemicla Measurements

Venous blood samples were taken in standardized fasting condition (12 h) in the morning (7–9 a.m.). Serum samples were stored frozen at −80 °C until analyzed. Serum triglycerides, total cholesterol and high-density lipoprotein (HDL) were determined by using KONELAB 20XTi analyzer (Thermo Fischer Scientific Inc, Waltham, MA, USA) and described previously [18]. The intra- and inter-assay correlation coefficients (CVs%) were 3.4% and 2.9% for triglycerides. Serum leptin was assessed using human leptin (ELISA; Diagnostic Systems Laboratories, Inc., Webster, TX, USA). The inter- and intra-assay coefficients of variation (CVs%) were 2.2% and 2.7% for leptin, respectively.

2.6. Fecal Samples

Fecal samples were taken from evacuated stool by the subjects with detailed guidance and frozen immediately and stored at −70 °C until processing. The bacterial cells were separated and analyzed with a previously described method using 16S rRNA hybridization, DNA-staining, and flow cytometry [19,20]. The following five 16S rRNA-targeted oligonucleotide probes labelled at the 5′-end with Cy5 indocarbocyanine (Ex/Em 646/662 nm; Molecular Probes, Eugene, OR, USA) were used: Bacto1080 for Bacteroides group, Bif164 for Bifidobacterium species, Enter1432 for Enterobacteria (Ent), EreC482 for Eubacterium rectale-Clostridium coccoides group (EreC group) and Fprau645 for Faecalibacterim prausnitzii (F.p).

2.7. Statistical Analyses

Data analyses were carried out using SPSS Statistics 22 for Windows (SPSS Inc., Chicago, IL, USA). All data was checked for normality using the Shapiro–Wilk W-test. If data were not normally distributed, their natural logarithms were used. Multiple comparisons were applied by using analysis of variance (ANOVA) with the Sidak post-hoc test.
Linear regression was used to assess the relationship between cardio-respiratory fitness and gut microbiota. The analyses were performed with and without adjusting for age, dietary intake and fat%. The p-values were adjusted by multiple comparisons in FDR (false discovery rate). Statistical significance was set at p < 0.05 with 2-tails.
In addition, multivariable least square regressions were performed in assessing the contribution of the variables (i.e., EreC, age, energy yield nutrients of fat, carbohydrates, protein and alcohol, as well as fat% of the whole body) and the outcome variables (i.e., VO2max, leptin, HDL and TG). To make each outcome comparable, we standardized each column by means of z-scoring so that each column has mean value 0 and standard deviation 1. The Pearson correlation coefficients between the regressed outcome and the observed outcome were calculated and regression weights for each variable were provided. The higher the absolute weight, the higher the contribution.

3. Results

3.1. Descriptive Statistics

The physical characteristics of the study participants are given in Table 1. The high aerobic fitness group was relatively younger than the low aerobic fitness and control groups (p < 0.01). The high aerobic fitness group weighed less, had lower BMI and fat% and were more active than the other two groups (p < 0.005). No significant differences in energy yield nutrients (carbohydrate, protein, fat) and fiber intake were found among the groups. The high aerobic fitness group had lower serum triglycerides and leptin (p < 0.05), and tended to have lower cholesterol concentration than the low aerobic fitness group.

3.2. Comparison of Microbial Composition in Different Fitness Groups

The microbiota composition of the different fitness groups are presented in Figure 1. Using multivariable comparison, we found that the high fitness group had higher proportions of Bacteroides (p = 0.038) but lower EreC (p = 0.040) than the low fitness group. No significant differences were found in Bifidobacterium, Enterobacteria and Faecalibacterium prausnitzii among the groups.

3.3. Associations of Certain Bacterial Groups with Different Variables

The associations of certain bacterial groups with VO2max, fat%, and energy yield nutrients are presented in Figure 2. We found that EreC was inversely correlated with VO2max (p = 0.010, Figure 2a) and carbohydrate intake (p = 0.034, Figure 2c), but positively with fat% (p = 0.002, Figure 2b) and fat intake (p = 0.034, Figure 2d). EreC was also negatively correlated with HDL (p = 0.028), but positively with triglycerides (p = 0.002) and leptin (p = 0.001, Table 2). No other bacterial groups were correlated with VO2max. On the other hand, VO2max was negatively correlated with fat% (r = −0.755, p < 0.001), triglycerides (r = −0.274, p = 0.021) and leptin (r = −0.574, p < 0.001). After adjusting for the confounding factors of age and energy yield of macronutrient intakes (carbohydrate, protein, fat and alcohol), the associations between bacteria and metabolic traits remained (Table 2). However, when fat% was included in the model, the significant differences between EreC and VO2max, triglycerides and leptin disappeared.
In addition, each variable contribution to the multivariable associations between variables X (i.e., EreC, age, energy yield nutrients of fat, carbohydrates, protein and alcohol, as well as fat% of the whole body) and outcomes Y (i.e., VO2max, leptin, HDL and TG) are given in the Table 3. The Pearson correlation coefficient between the regressed outcome antable d the observed outcome are 0.838, 0.813, 0.416, and 0.551, respectively, with significant p < 0.001 for all. EreC contributed the most to the triglycerides followed by HDL, leptin and VO2max. Fat% has the highest contribution to VO2max.
Further, we performed a multi-variable regression analysis between the five intestinal bacteria (X) and the five outcomes (Y). The contribution weights are listed in Table 4. The Pearson correlation coefficients between the regressed outcome and the observed outcome are 0.350, 0.428, 0.442, 0.431, and 0.477, with a significance of 0.003, <0.001, <0.001, <0.001, and <0.001, respectively. EreC has the highest contribution among all the bacteria to the outcomes (VO2max, fat%, leptin, HDL and TG, respectively).

4. Discussion

In this study, we found that gut microbiota was associated with cardiorespiratory fitness in young and middle-aged women. The study participants with low aerobic fitness had higher Eubacterium rectale-Clostridium coccoides and Enterobacteria, but lower Bacteroides. These differences were independent of age and macronutrient intake, but appeared to be confounded by adiposity, since all differences between the groups disappeared after adjusting for the percent of body fat.
There is growing interest in gut microbiota and their role in health and disease. However, research in this field is still in the beginning phase, therefore, the results are inconclusive but illustrative. It has been shown that obese mice had significantly lower Bacteroidetes but a higher proportion of Firmicutes compared to lean mice [21]. A similar observation was also found in a human study when comparing obese to lean twins [22]. Previous studies also showed that the EreC group is associated with obesity and related to metabolic disorders [23,24,25,26]. Our current results are in agreement with these studies by showing that the low VO2max group (which had a high BMI) had significantly lower Bacteroides but higher EreC (phylum Firmicutes) and Enterobacteria (Phylum Proteobacteria). EreC contributes the most to the regression of VO2max. However, Goodrich et al. found in the large TwinsUK population study that Bacteroidales (phylum Bacteroidetes) and family Clostridiaceae (phylum Firmicutes) correlated negatively with BMI and triglycerides [27]. Similar results were reported in another large population-based cohort by Fu et al. [28] The discrepancy between these studies may partly be due to differences in sample size, study population and the methods used to assess the bacteria, it might also be because different bacterial species (within the same phyla) have different functions and thus also different associations. In addition, the intestinal microbiota is known to regulate host energy homeostasis and can be influenced by diet and other environmental factors which are still not well characterized [28].
Lifestyle factors such as diet and exercise contribute largely to obesity and other cardio-metabolic disorders. Several studies have shown that a low carbohydrate diet can decrease EreC group proportions [29,30]. A decreased Firmicutes to Bacteroidetes ratio was found in an intervention with a calorie-restricted diet [31]. Our result showed that the EreC group was negatively correlated with carbohydrate intake but positively with fat intake. No association was found between the EreC group and fiber intake. Earlier studies have indicated that a dietary-resistant (or “non-digestive”) carbohydrate—through microbial conversion to short-chain fatty acids (SCFA)—is less effective than the equivalent amount of sugar absorbing directly in the small intestine for harvesting the energy [29]. SCFAs serve as energy substrates for the epithelial cells of the gut and provide part of the dietary energy [29,32], while other studies have shown that microbial pathways which generate SCFAs were enriched in metagenomics studies of obese subjects, and levels of SCFAs were elevated in overweight or obese people and animal models [25,33]. Hence the changes in gut microbiota composition that result from different dietary intakes are still inclusive.
The underlying mechanisms by which cardiorespiratory fitness might be associated with microbiota are yet to be fully understood. A recent study by Estaki et al. [10] showed that cardiorespiratory fitness is associated with increased gut microbial diversity. The association between cardiorespiratory fitness and certain bacteria in our study was confounded by adiposity, which raised the question of whether cardiorespiratory fitness plays a role in altering gut microbiota. One of the possible links between cardiorespiratory fitness and certain bacteria may be that physical activity decreases total colonic transit time. Long-term regular physical activity (resulting in high cardiorespiratory fitness) has a positive effect on both constipation indices and rectosigmoid transit time [34,35]. An earlier study has showed that a short gastrointestinal tract (GIT) resulting from gastric bypass enriched the gut microbiota of phylum level Bacteroidetes, Verrucomicrobia, and Proteobacteria after the surgery [36]. It is possible that the speed of colonic transit might be higher in those subjects who had higher VO2max. Furthermore, physically active individuals are more likely to follow a healthy lifestyle and to be more exposed to their environmental biosphere, and this may also contribute to a different composition of microbiota. Simultaneously, adaptation and the acute effect of endurance training can lead to changes in the GIT, such as increased transit and absorptive capacity, tissue hypoxia, and decreased blood flow [37,38]. These and other potential mechanisms, such as changes in gut pH, may create an environmental setting which limits the growth of the EreC group. On the other hand, controlling for fat%, the association between the EreC group and VO2max disappeared and fat% contributes the most in the regression model. Hence, whether exercise or fitness level has significant impact on the time of chyme staying in the intestinal tract, altering the composition of gut microbiota, deserves further study.
An earlier study has reported that the gut microbiome contributes to a substantial proportion of the variation in blood lipids [28,39]. Gut microbiota have been linked with lipid metabolism through their role in bile acid metabolism [40]. Fu et al. used 16s rRNA gene sequencing and found that members of family Ruminococcaceae (OTU 175962, 1703711, 295743, 178385) were negatively correlated with triglycerides, but certain members of the genus Eubacterium (OTU 49837) positively correlated with triglycerides [28]. However, Karlsson et al. used shotgun sequencing to find that the Clostridium species, Ruminococcus species and Eubacterium rectale were correlated positively with triglycerides and leptin, and negatively with HDL [41]. We found that a high proportion of EreC was associated with high triglycerides, but low HDL. Controlling for fat%, the significant associations between the EreC group and triglycerides remained, but disappeared between the EreC group and HDL. Since the EreC group includes members of Clostridium oroticum, Clostridium nexile, Ruminococcus hansenii, Ruminococcus productus, and Eubacterium rectale, it indicates the complexity of the bacterial in relation to the clinical outcomes. Nerveless, this could partly explain why different associations were reported for the EreC group with triglycerides and HDL.
Our study had some limitations. First, the study used a cross-sectional design and therefore no cause and effect relationship could be identified. Second, the method we used to assess gut microbiota was based on the detection of conserved regions of the 16S ribosomal RNA gene [42]. Though the method we used can detect the specific groups and clusters of gut microbiota [43,44,45,46], it cannot reach the deeper taxonomical levels such as species and strains, which might be important in the assessment of the functional role of intestinal microbiota. Thus, a more detailed structural and functional analysis of gut microbiota based on, for example, next generation sequencing methods in a larger scale of the population is required in order to explore the entire gut microbiota community [47]. One other limitation is that most of our participants were overweight or obese with relatively low fitness levels, this may have resulted in the underestimation of the association between VO2max and bacteria. Finally, only premenopausal women were included in this study, thus the effect of estrogen on the gut microbiota [11] cannot be verified. Nerveless, our results provide new information that there is a moderate association between physical fitness and certain gut microbiota in premenopausal women.

5. Conclusions

In conclusion, a low proportion of EreC in the gut is associated with high aerobic fitness in women, independent of age and dietary intakes. However, the relationship between VO2max and EreC is confounded by body fatness. In addition, the EreC group was positively associated with triglycerides. Our study indicates that further studies are needed to verify the relationships between gut microbiota, lipids and cardiorespiratory fitness.

Acknowledgments

Funding for this study was provided by the Academy of Finland (Grant 24302031), China State Sport General Administration (2015B039), the National Natural Science Foundation of China (NSFC 31571219), and the Shanghai Jiao Tong University Zhiyuan Foundation (CP2014013). The authors would like to thank Markku Alen, Shu Mei Cheng and Satu Pekkala for their contribution to data collection.

Author Contributions

S.C. conceived and designed the experiments; P.W. and X.T. performed the experiments, E.M. contributed the fecal samples analysis; Y.Y. and Y.S. analyzed the data; Y.Y and S.C. drafted the manuscript which was reviewed and modified by Y.S., N.W., X.Z., O.T., C.Z., E.M. and S.C. All authors have read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

References

  1. Lynch, S.V.; Pedersen, O. The human intestinal microbiome in health and disease. New Engl. J. Med. 2016, 375, 2369–2379. [Google Scholar] [CrossRef] [PubMed]
  2. Clemente, J.C.; Ursell, L.K.; Parfrey, L.W.; Knight, R. The impact of the gut microbiota on human health: An integrative view. Cell 2012, 148, 1258–1270. [Google Scholar] [CrossRef] [PubMed]
  3. Gerard, P. Gut microbiota and obesity. Cell Mol. Life Sci. 2016, 73, 147–162. [Google Scholar] [CrossRef] [PubMed]
  4. Larsen, N.; Vogensen, F.K.; van den Berg, F.W.; Nielsen, D.S.; Andreasen, A.S.; Pedersen, B.K.; Al-Soud, W.A.; Sorensen, S.J.; Hansen, L.H.; Jakobsen, M. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS ONE 2010, 5, e9085. [Google Scholar] [CrossRef] [PubMed]
  5. Kelly, T.N.; Bazzano, L.A.; Ajami, N.J.; He, H.; Zhao, J.; Petrosino, J.F.; Correa, A.; He, J. Gut microbiome associates with lifetime cardiovascular disease risk profile among bogalusa heart study participants. Circ. Res. 2016, 119, 956–964. [Google Scholar] [CrossRef] [PubMed]
  6. Jiang, W.; Wu, N.; Wang, X.; Chi, Y.; Zhang, Y.; Qiu, X.; Hu, Y.; Li, J.; Liu, Y. Dysbiosis gut microbiota associated with inflammation and impaired mucosal immune function in intestine of humans with non-alcoholic fatty liver disease. Sci. Rep. 2015, 5, 8096. [Google Scholar] [CrossRef] [PubMed]
  7. Matsumoto, M.; Inoue, R.; Tsukahara, T.; Ushida, K.; CHIJI, H.; Matsubara, N.; Hara, H. Voluntary running exercise alters microbiota composition and increases n-butyrate concentration in the rat cecum. Biosci. Biotechnol. Biochem. 2008, 72, 572–576. [Google Scholar] [CrossRef] [PubMed]
  8. Duncan, S.H.; Belenguer, A.; Holtrop, G.; Johnstone, A.M.; Flint, H.J.; Lobley, G.E. Reduced dietary intake of carbohydrates by obese subjects results in decreased concentrations of butyrate and butyrate-producing bacteria in feces. Appl. Environ. Microbiol. 2007, 73, 1073–1078. [Google Scholar] [CrossRef] [PubMed]
  9. Clarke, S.F.; Murphy, E.F.; O'Sullivan, O.; Lucey, A.J.; Humphreys, M.; Hogan, A.; Hayes, P.; O'Reilly, M.; Jeffery, I.B.; Wood-Martin, R.; et al. Exercise and associated dietary extremes impact on gut microbial diversity. Gut 2014, 63, 1913–1920. [Google Scholar] [CrossRef] [PubMed]
  10. Estaki, M.; Pither, J.; Baumeister, P.; Little, J.P.; Gill, S.K.; Ghosh, S.; Ahmadi-Vand, Z.; Marsden, K.R.; Gibson, D.L. Cardiorespiratory fitness as a predictor of intestinal microbial diversity and distinct metagenomic functions. Microbiome 2016, 4. [Google Scholar] [CrossRef] [PubMed]
  11. Baker, J.M.; Al-Nakkash, L.; Herbst-Kralovetz, M.M. Estrogen-gut microbiome axis: Physiological and clinical implications. Maturitas 2017, 103, 45–53. [Google Scholar] [CrossRef]
  12. Ortega, F.B.; Ruiz, J.R.; Castillo, M.J.; Sjostrom, M. Physical fitness in childhood and adolescence: A powerful marker of health. Int. J. Obes. (Lond) 2008, 32, 1–11. [Google Scholar] [CrossRef] [PubMed]
  13. Shephard, R.J.; Allen, C.; Benade, A.J.; Davies, C.T.; Di Prampero, P.E.; Hedman, R.; Merriman, J.E.; Myhre, K.; Simmons, R. The maximum oxygen intake. An international reference standard of cardiorespiratory fitness. Bull. World Health Organ. 1968, 38, 757–764. [Google Scholar] [PubMed]
  14. Wiklund, P.K.; Pekkala, S.; Autio, R.; Munukka, E.; Xu, L.; Saltevo, J.; Cheng, S.; Kujala, U.M.; Alen, M.; Cheng, S. Serum metabolic profiles in overweight and obese women with and without metabolic syndrome. Diabetol. Metab. Syndr. 2014, 6, 40. [Google Scholar] [CrossRef] [PubMed]
  15. Tan, X.; Alen, M.; Cheng, S.M.; Mikkola, T.M.; Tenhunen, J.; Lyytikainen, A.; Wiklund, P.; Cong, F.; Saarinen, A.; Tarkka, I.; et al. Associations of disordered sleep with body fat distribution, physical activity and diet among overweight middle-aged men. J. Sleep Res. 2015, 24, 414–424. [Google Scholar] [CrossRef] [PubMed]
  16. Munukka, E.; Pekkala, S.; Wiklund, P.; Rasool, O.; Borra, R.; Kong, L.; Ojanen, X.; Cheng, S.M.; Roos, C.; Tuomela, S.; et al. Gut-adipose tissue axis in hepatic fat accumulation in humans. J. Hepatol. 2014, 61, 132–138. [Google Scholar] [CrossRef] [PubMed]
  17. Lyytikainen, A.; Lamberg-Allardt, C.; Kannas, L.; Cheng, S. Food consumption and nutrient intakes with a special focus on milk product consumption in early pubertal girls in Central Finland. Public Health Nutr. 2005, 8, 284–289. [Google Scholar] [CrossRef] [PubMed]
  18. Cheng, S.; Wiklund, P.; Autio, R.; Borra, R.; Ojanen, X.; Xu, L.; Tormakangas, T.; Alen, M. Adipose tissue dysfunction and altered systemic amino acid metabolism are associated with non-alcoholic fatty liver disease. PLoS ONE. 2015, 10, e138889. [Google Scholar] [CrossRef] [PubMed]
  19. Vaahtovuo, J.; Korkeamaki, M.; Munukka, E.; Viljanen, M.K.; Toivanen, P. Quantification of bacteria in human feces using 16S rRNA-hybridization, DNA-staining and flow cytometry. J. Microbiol. Methods 2005, 63, 276–286. [Google Scholar] [CrossRef] [PubMed]
  20. Vaahtovuo, J.; Munukka, E.; Korkeamaki, M.; Luukkainen, R.; Toivanen, P. Fecal microbiota in early rheumatoid arthritis. J. Rheumatol. 2008, 35, 1500–1505. [Google Scholar] [PubMed]
  21. Ley, R.E.; Backhed, F.; Turnbaugh, P.; Lozupone, C.A.; Knight, R.D.; Gordon, J.I. Obesity alters gut microbial ecology. Proc. Natl. Acad. Sci. USA. 2005, 102, 11070–11075. [Google Scholar] [CrossRef] [PubMed]
  22. Turnbaugh, P.J.; Hamady, M.; Yatsunenko, T.; Cantarel, B.L.; Duncan, A.; Ley, R.E.; Sogin, M.L.; Jones, W.J.; Roe, B.A.; Affourtit, J.P.; et al. A core gut microbiome in obese and lean twins. Nature 2009, 457, 480–484. [Google Scholar] [CrossRef] [PubMed]
  23. Cho, I.; Yamanishi, S.; Cox, L.; Methe, B.A.; Zavadil, J.; Li, K.; Gao, Z.; Mahana, D.; Raju, K.; Teitler, I.; et al. Antibiotics in early life alter the murine colonic microbiome and adiposity. Nature 2012, 488, 621–626. [Google Scholar] [CrossRef] [PubMed]
  24. Munukka, E.; Wiklund, P.; Pekkala, S.; Volgyi, E.; Xu, L.; Cheng, S.; Lyytikainen, A.; Marjomaki, V.; Alen, M.; Vaahtovuo, J.; et al. Women with and without metabolic disorder differ in their gut microbiota composition. Obesity 2012, 20, 1082–1087. [Google Scholar] [CrossRef] [PubMed]
  25. Turnbaugh, P.J.; Ley, R.E.; Mahowald, M.A.; Magrini, V.; Mardis, E.R.; Gordon, J.I. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 2006, 444, 1027–1031. [Google Scholar] [CrossRef] [PubMed]
  26. Guinane, C.M.; Cotter, P.D. Role of the gut microbiota in health and chronic gastrointestinal disease: understanding a hidden metabolic organ. Therap. Adv. Gastroenterol. 2013, 6, 295–308. [Google Scholar] [CrossRef] [PubMed]
  27. Shoaie, S.; Karlsson, F.; Mardinoglu, A.; Nookaew, I.; Bordel, S.; Nielsen, J. Understanding the interactions between bacteria in the human gut through metabolic modeling. Sci. Rep. 2013, 3, 2532. [Google Scholar] [CrossRef] [PubMed]
  28. Fu, J.; Bonder, M.J.; Cenit, M.C.; Tigchelaar, E.F.; Maatman, A.; Dekens, J.A.; Brandsma, E.; Marczynska, J.; Imhann, F.; Weersma, R.K.; et al. The gut microbiome contributes to a substantial proportion of the variation in blood lipids. Circ. Res. 2015, 117, 817–824. [Google Scholar] [CrossRef] [PubMed]
  29. Duncan, S.H.; Lobley, G.E.; Holtrop, G.; Ince, J.; Johnstone, A.M.; Louis, P.; Flint, H.J. Human colonic microbiota associated with diet, obesity and weight loss. Int. J. Obes. 2008, 32, 1720–1724. [Google Scholar] [CrossRef] [PubMed]
  30. Simoes, C.D.; Maukonen, J.; Scott, K.P.; Virtanen, K.A.; Pietilainen, K.H.; Saarela, M. Impact of a very low-energy diet on the fecal microbiota of obese individuals. Eur. J. Nutr. 2014, 53, 1421–1429. [Google Scholar] [CrossRef] [PubMed]
  31. Ley, R.E.; Turnbaugh, P.J.; Klein, S.; Gordon, J.I. Microbial ecology: Human gut microbes associated with obesity. Nature 2006, 444, 1022–1023. [Google Scholar] [CrossRef] [PubMed]
  32. Donohoe, D.R.; Garge, N.; Zhang, X.; Sun, W.; O'Connell, T.M.; Bunger, M.K.; Bultman, S.J. The microbiome and butyrate regulate energy metabolism and autophagy in the mammalian colon. Cell Metab. 2011, 13, 517–526. [Google Scholar] [CrossRef] [PubMed]
  33. Schwiertz, A.; Taras, D.; Schafer, K.; Beijer, S.; Bos, N.A.; Donus, C.; Hardt, P.D. Microbiota and SCFA in lean and overweight healthy subjects. Obesity. 2010, 18, 190–195. [Google Scholar] [CrossRef] [PubMed]
  34. De Schryver, A.M.; Keulemans, Y.C.; Peters, H.P.; Akkermans, L.M.; Smout, A.J.; De Vries, W.R.; van Berge-Henegouwen, G.P. Effects of regular physical activity on defecation pattern in middle-aged patients complaining of chronic constipation. Scand. J. Gastroenterol. 2005, 40, 422–429. [Google Scholar] [CrossRef] [PubMed]
  35. Peters, H.P.; De Vries, W.R.; Vanberge-Henegouwen, G.P.; Akkermans, L.M. Potential benefits and hazards of physical activity and exercise on the gastrointestinal tract. Gut 2001, 48, 435–439. [Google Scholar] [CrossRef] [PubMed]
  36. Liou, A.P.; Paziuk, M.; Luevano, J.J.; Machineni, S.; Turnbaugh, P.J.; Kaplan, L.M. Conserved shifts in the gut microbiota due to gastric bypass reduce host weight and adiposity. Sci. Transl. Med. 2013, 5, 141r–178r. [Google Scholar] [CrossRef] [PubMed]
  37. Gisolfi, C.V. Is the GI System Built For Exercise? News Physiol. Sci. 2000, 15, 114–119. [Google Scholar] [PubMed]
  38. Rosa, E.F.; Silva, A.C.; Ihara, S.S.; Mora, O.A.; Aboulafia, J.; Nouailhetas, V.L. Habitual exercise program protects murine intestinal, skeletal, and cardiac muscles against aging. J. Appl. Physiol. 2005, 99, 1569–1575. [Google Scholar] [CrossRef] [PubMed]
  39. Le Chatelier, E.; Nielsen, T.; Qin, J.; Prifti, E.; Hildebrand, F.; Falony, G.; Almeida, M.; Arumugam, M.; Batto, J.; Kennedy, S.; et al. Richness of human gut microbiome correlates with metabolic markers. Nature 2013, 500, 541–546. [Google Scholar] [CrossRef] [PubMed]
  40. Caspi, R.; Altman, T.; Billington, R.; Dreher, K.; Foerster, H.; Fulcher, C.A.; Holland, T.A.; Keseler, I.M.; Kothari, A.; Kubo, A.; et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2014, 42, D459–D471. [Google Scholar] [CrossRef] [PubMed]
  41. Karlsson, F.H.; Tremaroli, V.; Nookaew, I.; Bergstrom, G.; Behre, C.J.; Fagerberg, B.; Nielsen, J.; Backhed, F. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 2013, 498, 99–103. [Google Scholar] [CrossRef] [PubMed]
  42. Amann, R.I.; Krumholz, L.; Stahl, D.A. Fluorescent-oligonucleotide probing of whole cells for determinative, phylogenetic, and environmental studies in microbiology. J. Bacteriol. 1990, 172, 762–770. [Google Scholar] [CrossRef] [PubMed]
  43. Rigottier-Gois, L.; Bourhis, A.G.; Gramet, G.; Rochet, V.; Dore, J. Fluorescent hybridisation combined with flow cytometry and hybridisation of total RNA to analyse the composition of microbial communities in human faeces using 16S rRNA probes. Fems Microbiol. Ecol. 2003, 43, 237–245. [Google Scholar] [CrossRef] [PubMed]
  44. Rigottier-Gois, L.; Rochet, V.; Garrec, N.; Suau, A.; Dore, J. Enumeration of Bacteroides species in human faeces by fluorescent in situ hybridisation combined with flow cytometry using 16S rRNA probes. Syst. Appl. Microbiol. 2003, 26, 110–118. [Google Scholar] [CrossRef] [PubMed]
  45. Amann, R.I.; Binder, B.J.; Olson, R.J.; Chisholm, S.W.; Devereux, R.; Stahl, D.A. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl. Environ. Microbiol. 1990, 56, 1919–1925. [Google Scholar] [PubMed]
  46. Amann, R.I.; Ludwig, W.; Schleifer, K.H. Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol. Rev. 1995, 59, 143–169. [Google Scholar] [PubMed]
  47. Fraher, M.H.; O'Toole, P.W.; Quigley, E.M. Techniques used to characterize the gut microbiota: A guide for the clinician. Nat. Rev. Gastroenterol. Hepatol. 2012, 9, 312–322. [Google Scholar] [CrossRef] [PubMed]
Figure 1. The microbiota composition in different fitness groups. The error bars represent mean and standard deviation. The contrast estimates (p-values) by multivariable comparison with Sidak to localize the significant differences between the groups. * means p < 0.05. Bif = Bifidobacterium, F.p = Faecalibacterim prausnitzii, Bacto = Bacteroides, Ent = Enterobacteria, EreC = Eubacterium rectale-Clostridium coccoides.
Figure 1. The microbiota composition in different fitness groups. The error bars represent mean and standard deviation. The contrast estimates (p-values) by multivariable comparison with Sidak to localize the significant differences between the groups. * means p < 0.05. Bif = Bifidobacterium, F.p = Faecalibacterim prausnitzii, Bacto = Bacteroides, Ent = Enterobacteria, EreC = Eubacterium rectale-Clostridium coccoides.
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Figure 2. The associations of EreC group with VO2max, fat%, and energy yield (E%) nutrients. (a) EreC was inversely correlated with VO2max; (b) EreC was positively correlated with fat%; (c) EreC was inversely correlated with carbohydrate intake (E%); (d) EreC was positively correlated with fat intake (E%). Ln = log transferred values; EreC = Eubacterium rectale-Clostridium coccoides.
Figure 2. The associations of EreC group with VO2max, fat%, and energy yield (E%) nutrients. (a) EreC was inversely correlated with VO2max; (b) EreC was positively correlated with fat%; (c) EreC was inversely correlated with carbohydrate intake (E%); (d) EreC was positively correlated with fat intake (E%). Ln = log transferred values; EreC = Eubacterium rectale-Clostridium coccoides.
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Table 1. Physical characteristics, background and gut microbiota composition in the low, moderate and high cardiorespiratory fitness groups (means with 95% confidence intervals are given and contrast estimates (p-values) by multivariable comparison with Sidak to localize the significant differences between the groups).
Table 1. Physical characteristics, background and gut microbiota composition in the low, moderate and high cardiorespiratory fitness groups (means with 95% confidence intervals are given and contrast estimates (p-values) by multivariable comparison with Sidak to localize the significant differences between the groups).
VariablesLow VO2max Mean 95% CIModerate VO2max Mean 95% CIHigh VO2max Mean 95% CIANOVA (p-Value)
Number242324L-ML-HM-H
Age (years)40.4 (36.9–44.0)39.7 (35.5–43.8)30.6 (25.6–35.6)0.9910.0030.009
Height (cm)166.1 (164.0–168.3)166.7 (163.9–168.9)164.0 (161.7–166.3)0.9840.4900.307
Weight (kg)87.3 (83.2–91.5)77.6 (73.0–82.1)66.1 (62.1–70.1)0.004<0.0010.001
BMI31.7 (30.2–33.1)27.9 (26.7–29.1)24.6 (23.0–26.2)0.001<0.0010.004
Fat%40.6 (38.1–43.0)35.5 (33.2–37.8)28.0 (25.0–31.0)0.018<0.001<0.001
PA (time/week)1.39 (0.95–1.83)1.80 (1.22–2.38)3.44 (2.61–4.27)0.718<0.0010.001
PA (h/week)1.17 (0.69–1.64)2.11 (1.27–2.95)3.69 (2.82–4.56)0.195<0.0010.009
Fiber (g)20.4 (17.4–23.5)24.1 (20.6–27.6)21.0 (17.3–24.6)0.3230.9950.450
Carbohydrate (E%)47.1 (43.7–50.5)45.6 (41.7–49.5)44.7 (41.5–48.0)0.9010.6980.979
Protein (E%)18.4 (17.0–19.8)18.0 (17.0–19.0)18.0 (16.9–19.1)0.9370.9571.000
Fat (E%)32.8 (30.3–35.4)34.1 (30.9–37.2)35.2 (32.5–38.0)0.8870.5080.910
Alcohol (E%)1.73 (−54–3.99)2.38 (0.45–4.31)0.63 (−37–1.62)0.9580.9550.769
TG (mmol/L)1.56 (1.17–1.95)1.22 (1.02–1.42)1.01 (0.82–1.20)0.6080.0410.434
Cholestol (mmol/L)5.06 (4.55–5.56)5.03 (4.68–5.38)4.44 (4.14–4.74)0.9990.0720.098
HDL (mmol/L)1.42 (1.29–1.55)1.59 (1.47–1.72)1.57 (1.42–1.73)0.1980.2790.996
Leptin (ng/mL)56.8 (45.8–67.8)41.7 (34.5–48.8)26.9 (18.2–35.6)0.054<0.0010.062
BMI = body mass index; Fat% = percent body fat; PA = physical activity; E% = energy yield; TG = triglycerides; HDL = High-density lipoproteins.
Table 2. Linear regression of EreC with VO2max, fat%, leptin, HDL and TG.
Table 2. Linear regression of EreC with VO2max, fat%, leptin, HDL and TG.
EreCErec Adjusted for Age and Energy Yield Macronutrients Intakes of Carbohydrate, Protein, Fat and AlcoholEreC Adjusted for Age, Fat% and Energy Yield Macronutrients Intakes of Carbohydrate, Protein, Fat and Alcohol
rprprp
VO2max−0.3090.010−0.3390.006−0.1130.369
Fat%0.3820.0020.3640.005
Leptin0.4240.0010.3900.0030.1780.208
HDL−0.2600.028−0.3130.010−0.2330.124
TG0.3900.0020.3930.0030.3000.060
Fat% = percent body fat; HDL = High-density lipoproteins; TG = triglycerides; EreC = Eubacterium rectale-Clostridium coccoides; p value was adjusted by multiple comparisons in FDR (false discovery rate).
Table 3. The regression weights of multivariable associations.
Table 3. The regression weights of multivariable associations.
YVO2maxLeptinHDLTG
X
EreC−0.07110.1206−0.24940.3277
Age−0.3369−0.09170.15790.0618
Fat%−0.61630.7887−0.21300.3008
Carbohydrates (E%)−0.2886−0.26930.2631−0.1922
Protein (E%)−0.0680−0.1145−0.0252−0.0104
Fat (E%)−0.0520−0.12980.3869−0.1923
Alcohol (E%)−0.1851−0.10490.1990−0.3277
EreC = Eubacterium rectale-Clostridium coccoides; HDL = High-density lipoproteins; TG = triglycerides; Fat% = percent body fat; E% = energy yield.
Table 4. The regression weights of multivariable associations.
Table 4. The regression weights of multivariable associations.
YVO2maxFat%LeptinHDLTG
X
Bif0.03480.10920.0892−0.06790.1539
Bacto0.1536−0.1479−0.09390.2540−0.1184
F.p0.01130.0929−0.00800.1721−0.1377
Ent−0.0723−0.0129−0.00160.1995−0.0139
EreC−0.25680.32760.3942−0.31290.3894
Bif = Bifidobacterium, F.p = Faecalibacterim prausnitzii, Bacto = Bacteroides, Ent = Enterobacteria, EreC = Eubacterium rectale-Clostridium coccoides; Fat% = percent body fat; HDL = High-density lipoproteins; TG = triglycerides.

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