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 (VO) 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 VOas 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.

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.

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.

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., VO 2max , 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.

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 forgroup, Bif164 forspecies, Enter1432 for(Ent), EreC482 forgroup (group) and Fprau645 for(F.p).

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.

The maximum oxygen uptake (VO 2max 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 VO 2 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 VO 2max values, participants were divided into three groups by tertiles (low, moderate and high).

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.

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.

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.has the highest contribution among all the bacteria to the outcomes (VO, fat%, leptin, HDL and TG, respectively).

In addition, each variable contribution to the multivariable associations between variables X (i.e.,, age, energy yield nutrients of fat, carbohydrates, protein and alcohol, as well as fat% of the whole body) and outcomes Y (i.e., VO, 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< 0.001 for all.contributed the most to the triglycerides followed by HDL, leptin and VO. Fat% has the highest contribution to VO

The associations of certain bacterial groups with VO, fat%, and energy yield nutrients are presented in Figure 2 . We found thatwas inversely correlated with VO= 0.010, Figure 2 a) and carbohydrate intake (= 0.034, Figure 2 c), but positively with fat% (= 0.002, Figure 2 b) and fat intake (= 0.034, Figure 2 d).was also negatively correlated with HDL (= 0.028), but positively with triglycerides (= 0.002) and leptin (= 0.001, Table 2 ). No other bacterial groups were correlated with VO. On the other hand, VOwas negatively correlated with fat% (= −0.755,< 0.001), triglycerides (= −0.274,= 0.021) and leptin (= −0.574,< 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 betweenand VO, triglycerides and leptin disappeared.

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 (< 0.01). The high aerobic fitness group weighed less, had lower BMI and fat% and were more active than the other two groups (< 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 (< 0.05), and tended to have lower cholesterol concentration than the low aerobic fitness group.

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.

Bacteroidetes but a higher proportion of Firmicutes compared to lean mice [ EreC group is associated with obesity and related to metabolic disorders [24,25, 2max 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 VO 2max . 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 [ 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 lowerbut a higher proportion ofcompared 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 thegroup is associated with obesity and related to metabolic disorders [ 23 26 ]. Our current results are in agreement with these studies by showing that the low VOgroup (which had a high BMI) had significantly lowerbut higher(phylum) and(Phylumcontributes the most to the regression of VO. However, Goodrich et al. found in the large TwinsUK population study that(phylum) and family(phylum) 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 ].

EreC group proportions [ Firmicutes to Bacteroidetes ratio was found in an intervention with a calorie-restricted diet [ 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 [ 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 decreasegroup proportions [ 29 30 ]. A decreasedtoratio was found in an intervention with a calorie-restricted diet [ 31 ]. Our result showed that thegroup was negatively correlated with carbohydrate intake but positively with fat intake. No association was found between thegroup 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.

2max . 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 [ EreC group. On the other hand, controlling for fat%, the association between the EreC group and VO 2max 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. 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 VO. 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 thegroup. On the other hand, controlling for fat%, the association between thegroup and VOdisappeared 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.

Ruminococcaceae (OTU 175962, 1703711, 295743, 178385) were negatively correlated with triglycerides, but certain members of the genus Eubacterium (OTU 49837) positively correlated with triglycerides [ Clostridium species, Ruminococcus species and Eubacterium rectale were correlated positively with triglycerides and leptin, and negatively with HDL [ 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. 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(OTU 175962, 1703711, 295743, 178385) were negatively correlated with triglycerides, but certain members of the genus(OTU 49837) positively correlated with triglycerides [ 28 ]. However, Karlsson et al. used shotgun sequencing to find that thespecies,species andwere correlated positively with triglycerides and leptin, and negatively with HDL [ 41 ]. We found that a high proportion ofwas associated with high triglycerides, but low HDL. Controlling for fat%, the significant associations between thegroup and triglycerides remained, but disappeared between thegroup and HDL. Since thegroup includes members of, and, it indicates the complexity of the bacterial in relation to the clinical outcomes. Nerveless, this could partly explain why different associations were reported for thegroup with triglycerides and HDL.