To evaluate the effects of a multidisciplinary obesity treatment programme on fecal microbiota composition and immunoglobulin-coating bacteria in overweight and obese adolescents and their relationship to weight loss.
Longitudinal intervention study based on both a calorie-restricted diet (calorie reduction=10–40%) and increased physical activity (calorie expenditure=15–23 kcal/kg body weight per week) for 10 weeks.
Thirty-nine overweight and obese adolescents (BMI mean 33.1 range 23.7–50.4; age mean 14.8 range, 13.0–16.0).
BMI, BMI z-scores and plasma biochemical parameters were measured before and after the intervention. Fecal microbiota was analyzed by fluorescent in situ hybridization. Immunoglobulin-coating bacteria were detected using fluorescent-labelled F(ab′)2 antihuman IgA, IgG and IgM.
Reductions in Clostridium histolyticum and E. rectale-C. coccoides proportions significantly correlated with weight and BMI z-score reductions in the whole adolescent population. Proportions of C. histolyticum, C. lituseburense and E. rectale-C. coccoides dropped significantly whereas those of the Bacteroides-Prevotella group increased after the intervention in those adolescents who lost more than 4 kg. Total fecal energy was almost significantly reduced in the same group of adolescents but not in the group that lost less than 2.5 kg. IgA-coating bacterial proportions also decreased significantly in participants who lost more than 6 kg after the intervention, paralleled to reductions in C. histolyticum and E. rectale-C. coccoides populations. E. rectale-C. coccoides proportions also correlated with weight loss and BMI z-score reduction in participants whose weight loss exceeded 4 kg.
Specific gut bacteria and an associated IgA response were related to body weight changes in adolescents under lifestyle intervention. These results suggest interactions between diet, gut microbiota and host metabolism and immunity in obesity.
Obesity and the associated metabolic disorders, such as diabetes and metabolic syndrome, have become major public health issues in adult and paediatric populations worldwide.1, 2, 3 Obesity results from a positive energy balance and is characterized by a state of chronic, low-grade inflammation with abnormal cytokine and acute-phase inflammatory protein production.4 Treatments based on calorie restriction, exercise and behavioural changes have succeeded to some extent to control obesity, but usually yield limited and transient weight loss.5 More efficient strategies to control obesity and tackle its metabolic consequences are, therefore, urgently needed. In this context, it is essential to identify interactions between the environmental factors and host mechanisms involved in energy regulation with a view to developing additional intervention strategies.3, 6
The environmental factors accounting for the dramatic rise in obesity in recent decades are not fully understood. Breastfeeding seems to be a protective factor against obesity later in life, whereas increased energy intake in formula or mixed-fed infants seems to be detrimental.7 In recent studies, lack of breastfeeding, high early energy intake and high intake of sugar-sweetened beverages have also been shown to contribute to obesity in adolescents.8 In addition, shifts in the composition of gut microbiota in response to dietary factors, such as total quantity and quality of carbohydrate and fat intake, have been reported.9 In fact, the microbes populating the gut are currently being investigated as potential environmental factors involved in obesity.6, 9, 10 Gut microbiota is viewed as a metabolic organ that plays a pivotal role in the physiology of energy homeostasis.11 Commensal bacteria contribute to the digestion of nutrients otherwise inaccessible to humans, such as complex polysaccharides. The microbial fermentation of undigested dietary compounds in the large intestine can provide up to 10–15% of human daily energy supply.11 Gut microbes are known to be involved in the absorption of monosaccharides and short-chain fatty acids, as well as in their conversion to complex lipids in the liver and their storage in adipocytes.6 In addition, commensal bacteria colonizing the gut or in transit may also regulate the signalling pathways that link obesity with inflammation by interacting with the epithelium and host immune system.12 So far, some studies associated obesity with an increase in the proportion of Firmicutes and a reduction in Bacteroidetes, in mice obesity models and adult human participants through small-scale intervention studies10 but other results were controversial.13 In mice fed on a high-fat diet, increases in Bifidobacterium levels achieved by intake of prebiotics were correlated with normalization of inflammatory status and endotoxaemia.12 Notable differences in microbiota composition have also been shown between exercised and sedentary rats.14 Nevertheless, the associations between specific gut bacteria and human host metabolism and immunity in relation to obesity remain largely uncharacterized.
The objective of this study was to evaluate the effects of a multidisciplinary obesity treatment programme (including energy-restricted diet and increased physical activity) on fecal microbial composition and immunoglobulin-coating bacteria in overweight or obese adolescents and assess their relationship to biochemical parameters and weight loss. Thus, stronger links between gut microbes and human obesity can be established.
Participants and experimental design
Participants for the study were selected according to their body mass index (BMI) (weight (kg)/(height (m)2)) and classified as overweight or obese according to the International Obesity Task Force criteria defined by Cole et al.1 during the course of the EVASYON study. The current study was designed to develop a multidisciplinary obesity treatment programme adapted to Spanish primary health care centres and was assessed by Paediatric services in five cities around Spain. The treatment programme included nutritional and individual diet counselling, including calorie restriction and increased physical activity, as well as group therapies aim at changing behaviour, providing support and encouraging adolescents to change lifestyle and follow treatment recommendations. A total of 39 overweight or obese Spanish adolescents (20 female and 19 male participants; mean age 14.8 years) were included in this study, and their characteristics are shown in Table 1. BMI z-scores were calculated as a function of the participant's obesity degree when compared with BMI local reference standards.2 Over a 10-week period, the participants followed an energy-restricted diet (a 10–40% reduction) established according to both obesity degree and regular physical activity determined by accelerometry.15 The maximum energy intake was 1800 kcal day−1 for female participants and 2200 kcal day−1 for male participants. The physical activity programme was established to increase energy expenditure by 15–23 kcal kg−1 body weight per week. None of the volunteers were treated with antibiotics during the study.
Energy food intake
To determine the intake of energy food, diary records were kept for 72 h (2 weekdays and 1 weekend day) both before the start of the study (baseline intakes) and after the intervention (week 10). Detailed information on how to record food and drink consumed using common household measures was provided. Food diary records were returned to their dietician, and analyzed for energy contents based on the CESNID food composition database of Spanish foods.16
Fasting plasma glucose, total cholesterol, triglycerides and HDL cholesterol were measured by enzyme-colorimetric automated methods (Roche, Neuilly sur Seine Cedex, France). LDL cholesterol was calculated by the Friedwald equation. Fasting plasma insulin was measured by the LINCOplex KIT Human Gut Hormone Panel (CAT-HGT-68 K, Linco Research-St Charles, MO, USA).
Fecal sample collection and preparation for microbiological analyses
Fecal samples were collected at baseline and after 10 weeks of the intervention, frozen immediately after collection at −20 °C, and stored until analyzed. Feces were diluted 1:10 (w v−1) in PBS (pH 7.2) and homogenized in a Lab Blender 400 stomacher (Seward Medical London, UK) for 5 min. After low-speed centrifugation (2000 g, 2 min), the supernatant was collected. For bacterial quantification, cells were fixed by adding 4% paraformaldehyde solution (Sigma, St Louis, MO, USA) and incubated overnight at 4 °C. After fixation, bacteria were washed two times in PBS by centrifugation (12 000 g for 5 min). Finally, cell pellets were suspended in a PBS/ethanol mixture (1:1) and stored at −80 °C until analyzed as described earlier.17
Fluorescent in situ hybridization for microbiological analysis
The bacterial groups present in feces were quantified by fluorescent in situ hybridization (FISH) using group-specific probes (MOLBIOL, Berlin, Germany). The specific probes and controls used in this study, as well as the hybridization conditions, are shown in Table 2. The EUB 338 probe, targeting a conserved region within the bacterial domain, was used as a positive control,16 and the NON 338 probe was used as a negative control to eliminate background fluorescence.17 Control probes were covalently linked at their 5′end either to indocyanine dye Cy3 or to fluorescein isothiocyanate (FITC). Specific cell enumeration was performed by combining each of the group-specific FITC-probes with the EUB 338-Cy3 probe as described.17 Briefly, fixed cell suspensions were incubated in the hybridization solution (10 mM Tris-HCl, 0.9 M NaCl, pH 8.0 and 10% SDS) containing 4 ng μl−1 of each fluorescent probe at appropriate temperatures, overnight. Then, hybridized cells were pelleted by centrifugation (10 000 g for 5 min) and resuspended in 500 μl PBS solution for flow-cytometry analysis. The proportion of each bacterial group was expressed as a ratio of cells hybridizing with the FITC-labelled specific probe to cells hybridizing with the EUB 338-Cy3 probe.17
Immunoglobulin-coating bacterial analysis
Bacterial cells from 20 μl of the supernatant obtained after low-speed centrifugation were collected (10 000 g for 5 min). The pellet was resuspended in 60 μl 1% (w v−1) BSA/PBS, containing 1% (v v−1) FITC-labelled F(ab′)2 antihuman IgA, IgG or IgM (CALTAG Laboratories, Burlingame, CA, USA). Another aliquot of each sample was pelleted and resuspended in 60 μl 1% (w v−1) BSA/PBS and used as control. After 30 min incubation, suspensions were washed two times with PBS. Bacterial pellet was finally resuspended in 500 μl PBS and mixed with 20 μl propidium iodine (100 mg l−1) to label total bacteria before flow-cytometry detection.28
Flow-cytometry detections were performed using an EPICS XL-MCL flow cytometer (Beckman Coulter, FL, USA) as described earlier.17 This instrument is equipped with two light scatter detectors that measure forward (FSC) and side scatter (SSC) and fluorescence detectors that detect appropriately filtered light at green (FL1, 525 nm) and red–orange (FL3, 620 nm) wavelengths. The event rate was kept at the lowest setting (200–300 events per second) to avoid cell coincidence. A total of 15 000 events were recorded in a list mode file and analysed with the System II V.3 software (Beckman Coulter). The proportion of each bacterial group was expressed as a ratio of cells hybridizing with the FITC-labelled specific probe to cells hybridizing with the universal EUB 338-Cy3 probe.17 Immunoglobulin-coating fecal bacteria was expressed as a ratio of bacterial cells labelled with FITC-labelled F(ab′)2 antihuman IgA, IgG or IgM to the total cell population hybridizing with propidium iodine.28
Fecal energy determination
Energy content of feces was determined by calorimetry as described elsewhere29 using an Automatic Adiabatic Bomb Calorimeter (Gallenkamp, Leicestershire, UK). Fecal samples were dried by lyophilization and samples of 1.5 g dry weight were analyzed in duplicate. Gross energy content of fecal samples was defined as the amount of heat developed by the total combustion of a unit of dry weight sample.
Statistical analyses were done using the SPSS 11.0 software (SPSS Inc, Chicago, IL, USA). Results are expressed as median values and ranges determined in duplicate. Total bacteria, Gram-positive and Gram-negative bacteria were calculated by adding the proportions of the corresponding groups detected by specific probes, which do not overlap. Thus, total Gram-positive bacteria was calculated by adding the proportions obtained with the probes Chis150, Erec0482, Bif164, Clit135 and Lab158 and total Gram-negative was calculated by adding the proportions obtained with the probes Bac303, Ent1432 and SBR687. Differences in bacterial populations immunoglobulin-coating bacteria, fecal energy and biochemical parameters detected before and after the intervention programme were determined using the Mann–Whitney U-test of non-normal data distribution. The Spearman's correlation test was used to calculate the correlations between bacterial count changes and either weight loss or biochemical changes as a result of the intervention. In every case, a P-value <0.05 was considered statistically significant.
Statement of ethics
We certify that all applicable institutional and governmental regulations concerning the ethical use of human volunteers were followed during this research. Informed consent was obtained from all adolescents and their parents, and the study was approved by the local Ethics Committees.
Clinical characteristics did not differ significantly between the two groups of adolescents (A and B) at recruitment time (Table 1). Base line values of BMI and BMI z-score of group A were 30.7 (26.4–36.3) and 2.95 (1.6–4.03), respectively and those of group B were 33.1 (30.0–35.0) and 3.22 (2.57–4.16), respectively. The participants, 51% female (20/39) and 49% male (19/39), were 14.4 years old (13.0–16.0 years), maintained an apparently good health status and did not consume antibiotics during the study (Table 1). Most of the participants (n=26) experienced significant (P=0.050) weight loss from 4.1 to 16.6 kg (mean decrease of 7.6 kg) after 10 weeks of following the intervention programme. Some of them (n=13) did not experience remarkable weight loss (mean decrease of 1.1 kg; range 0.8–2.4, P=0.798). These two groups showed significant differences in their weight loss (P<0.001) and BMI z-score reduction (P<0.001) and, accordingly, were subdivided for comparisons of their fecal bacterial populations into groups A (>4 kg, mean 7.6 kg weight loss) and B (<2.5 kg, mean 1.1 kg weight loss). BMI and BMI z-score (P=0.033 and P=0.039, respectively) detected before and after the dietary intervention was also significantly different in group A but not in group B. Group A was further subdivided into other two groups that also displayed significant differences in weight loss (P<0.001), one (group A1) integrated by participants with a weight loss of 4–6 kg (corresponding to a 5.5% decrease in body weight) and the other (group A2) integrated by those participants with a weight loss exceeding 6 kg (corresponding to 9.4% decrease in body weight) for comparisons of fecal immunoglobulin coating bacteria as indicated below.
The dietary intervention resulted in a significant reduction (P<0.050) in total energy intake in both adolescent groups from 2284 (2739–1549) to 1429.4 (1049–1782) kcal day−1 in group A and from 2159 (1926–2414) to 1416 (1296–1508) kcal day−1 in group B. No significant differences in dietary energy intake were found between both adolescent groups before and after the intervention programme.
Microbiota composition and energy of feces from adolescents
A follow-up study was made of the shifts in composition of fecal microbiota of the participants under study during the weight loss intervention programme. The results of the fecal microbiota analyses before and after intervention by FCM–FISH techniques are shown in Figures 1, and 2 and Table 3. In the whole adolescent population, the intervention programme led to reductions in Clostridium histolyticum proportions, which correlated with weight loss (Figure 1a; r=0.43; P=0.009), as did reductions in E. rectale-C. coccoides proportions (Figure 1b; r=0.50, P=0.001). Similar correlations were found between C. histolyticum and E. rectale-C. coccoides proportions and BMI z-scores (r=0.41, P=0.012 and r=0.39; P=0.014, respectively). Bacteroides proportions increased as a result of the intervention and almost reached significant levels of correlation with weight loss (Figure 1c; r=−0.28; P=0.083). Although increases in Lactobacillus–Enterococcus proportions were also parallel to reductions in weight (Figure 1d; r=−0.15; P=0.361) and BMI z-scores (r=−0.29; P=0.074), correlation was not significant. Shifts in Bifidobacterium, C. lituseburense, Enterobacteriaceae, Escherichia coli, Roseburia and sulphate-reducing bacterial groups were neither significantly correlated with weight loss nor with BMI z-score reductions.
The composition of the fecal microbiota of two groups of adolescents A and B, which displayed significant differences in weight loss (>4 kg in group A versus <2.5 kg in group B) and BMI z-score reduction after intervention, was compared at base line, revealing the presence of significantly higher levels (P=0.008) of Lactobacillus proportions in the group A than in group B (Table 3). In group A, Gram-positive bacterial populations, estimated by adding the proportions of corresponding groups targeted by the probes, were significantly lower (P=0.046) after the intervention, whereas significant differences in Gram-negative bacteria and total bacteria were not detected (Figure 2a). In contrast, in group B no differences were detected in Gram-positive, Gram-negative or total bacteria proportions present in feces before and after the intervention (Figure 2b).
In group A, which experienced important weight loss (>4 kg) and BMI z-score reductions (mean decrease 1.05; range 1.86–0.38), C. histolyticum, C. lituseburense and E. rectale-C. coccoides proportions decreased significantly after the intervention programme (P=0.011, P=0.049 and P=0.033, respectively), whereas those of Bacteroides-Prevotella group were significantly increased (P=0.047). Lactobacillus–Enterococcus, Enterobacteriaceae, E. coli and Roseburia groups showed slight increases whereas Bifidobacterium and sulphate-reducing bacteria tended to decrease but these changes were not statistically significant. Reduced C. histolyticum and E. rectale-C. coccoides proportions significantly correlated with percentage of body weight loss (r=0.48; P=0.020 and r=0.41; P=0.036, respectively) and those of E. rectale-C. coccoides with BMI z-score reductions (r=0.36; P=0.020 and r=0.41; P=0.036, respectively). In the adolescents group B, who did not experience a significant weight loss, none of the analyzed bacterial groups showed statistically significant differences before and after the intervention programme (Table 3). No correlations were detected between bacterial proportions and either body weight or BMI z-score reductions.
Fecal energy content before (5.43 (5.11–5.90) kcal g−1) and after (5.16 (4.94–5.28) kcal g−1) the intervention was almost significantly reduced (P=0.055) in group A of adolescents. In contrast, fecal energy content before (5.42 (5.04–5.66) kcal g−1) and after (5.33 (5.2–5.42) kcal g−1) the intervention was not significantly different (P=0.513) in group B of adolescents.
Biochemical parameters and correlations with fecal microbiota
Biochemical parameters in both groups of adolescents before and after the intervention are shown in Table 4. No significant differences were found in the analyzed biochemical parameters between both adolescent groups A and B before the intervention (base line) except for LDL levels, which were higher in group B of adolescents (P=0.034). Serum HDL-cholesterol values were significantly higher (P=0.031) before than after the intervention in group B of adolescents but did not correlate with any bacterial group changes. Slight changes in serum glucose concentration correlated with slight changes in E. rectale-C. coccoides as a result of the intervention in group B of adolescents (r=0.683, P=0.030). Serum glucose (P=0.029) and total cholesterol (P=0.012) concentration significantly dropped in group A of adolescent after the intervention. Changes in glucose and cholesterol significantly correlated with changes in the enteric group proportions (r=−0.547, P=0.006 and r=−0.462, P=0.035, respectively). In addition, changes in glucose significantly correlated with changes in total Gram-negative bacteria (r=−0.538, P=0.012). LDL cholesterol was reduced after the intervention although not significantly and correlated to changes in C. lituseburense proportions (r=−0.508, P=0.019).
Immunoglobulin-coating bacteria in feces from adolescents
Immunoglobulin-coating bacteria were detected in feces of adolescents who experienced the greatest loss (>4 kg; group A) in body weight (Figure 3). Overall, higher percentages of IgA, IgM and IgG-coating bacteria were detected in feces of adolescents before the intervention than after it. The proportions of IgA-coating bacteria were significantly reduced (P=0.034) after the intervention programme in group A1 of participants, who lost over 6 kg, whereas these differences were not significant in group A2 of participants with weight reductions between 4 and 6 kg (Figure 3). Group A1 also revealed significant reductions in proportions of C. histolyticum and E. rectale-C. coccoides groups (P=0.046 and P=0.044, respectively) whereas changes in group A2 did not reach statistical significance (P<0.050), indicating that these bacteria exerted the greatest influence on IgA-coating bacterial changes. In addition, reduced E. rectale-C. coccoides proportions in group A1 significantly correlated with the percentage of body weight loss and BMI z-score reduction (r=0.56; P=0.023 and r=0.53; P=0.035, respectively). Group A1 also showed significantly lower proportions of total Gram-positive bacterial populations (P=0.034) after the intervention programme, whereas no significant differences in Gram-negative bacteria and total bacteria were detected. Therefore, changes in IgA secretion against the gut microbiota could be explained by specific changes in its composition associated with weight loss.
Diet is considered to be one of the main environmental factors shaping the composition of the gut microbiota within a host and affecting their functional relationships. This study has demonstrated that a weight loss intervention programme, based on a calorie-restricted diet and increased physical activity, induced changes in the gut microbiota structure of obese adolescents and that some of these changes correlated with weight loss and BMI z-score reductions. Therefore, the relative abundance of specific gut bacteria seems to be susceptible to lifestyle intervention and may be an additional element for consideration in weight management strategies.
Reduced E. rectale-C. coccoides and C. histolyticum proportions were significantly correlated with weight loss and BMI z-score reduction in the total population. By contrast, Bacteroides proportions increased as a result of the intervention programme, although their correlations with weight loss and BMI z-scores did not reach statistical significance. In previous human studies in adults, decreases in Firmicutes division, which include Clostridium clusters, as well as increases in Bacteroidetes division have also been correlated with the percentage of body weight loss; however, a very limited number of participants were included in the corresponding study.10 Therefore, the present report has confirmed that the abundance of these two bacterial groups in the distal gut could be linked to human weight loss although controversial results have also been reported recently.13
Specific fecal bacterial proportions differed significantly in group A of adolescents, who experienced a remarkable weight loss (>4 kg) representing on average 8.1% of their body weight, as a result of the intervention. Accordingly, previous studies have indicated that Firmicutes and Bacteroides changes are associated with weight loss percentage. These changes were only evident when the individuals had lost at least between 2 and 6% of their body weight, without finding a relationship to the type of diet (either fat or carbohydrate restricted).10 Overall, total Gram-positive bacterial populations were significantly reduced and Gram-negative bacteria slightly increased after the intervention programme in group A of adolescents. C. histolyticum, E. rectale-C. coccoides and C. lituseburense were identified as the main contributors to the overall reduction in Gram-positive bacteria, whereas Bacteroides group contributed to the increase in total Gram-negative bacteria.
These gut microbes could play a role in obesity together with diet by affecting either host metabolism,6 or the signalling pathways that link inflammation with obesity.12 E. rectale-C. coccoides group includes clostridia cluster XIV, which integrates the main butyrate-producing bacteria in the distal colon.29 These bacteria are responsible for generating butyric acid from carbohydrate fermentation, which fuels epithelial cells covering up to 70% of their energy needs.30 The reduction of these bacterial groups by intervention in obese adolescents could contribute to reducing the overall ability of the gut microbiota to harvest energy from the diet, which could account for up to 10–15% of our daily caloric supply.31 Nevertheless, butyrate generation by gut microbes has generally been associated with beneficial effects, including satiety promotion, rather than with obesogenic features,32 indicating that more complex mechanisms related to fatty-acid metabolism could link Firmicutes and Clostridium clusters with obesity.
C. histolyticum proportions were also reduced after the intervention programme in adolescents and these shifts were correlated with weight loss. This group belongs to clostridia cluster II, which are highly proteolytic and produce acetate as the main end product of metabolism.33 Increased levels in colonic and serum acetate, which may stimulate lipid synthesis, have also been associated with microbial gut colonization.34, 35 In addition, C. histolyticum produces proteases that are cytotoxic for cells and tissues and could be pathogenic factors in the gut environment. The prevalence of this clostridial group could also increase protein fermentation in the colon with the subsequent generation of toxic compounds such as sulphur-containing metabolites.11 C. lituseburense group, which is included in clostridia cluster XI, was also decreased after the intervention in group A of adolescents. This is a heterogeneous phylogenetic cluster but comprises opportunistic pathogens, such as Clostridium difficile, and its abundance together with that of C. histolyticum group could modify the potential virulence of the gut microbiota in obese patients, which in turn was improved in adolescents showing the highest weight loss.33 In contrast to Clostridium groups, Bacteroides were increased in individuals showing a significant weight loss (<4 kg) after the dietary intervention, which may be related to changes in the type of short-chain fatty acid-generated and colonic pH increases.36 Increased Bacteroides populations could contribute to generating propionate, which has been shown to inhibit lipid synthesis from acetate and may favour a lean phenotype.35, 37
This study has also showed almost significant changes in total fecal energy in group A of adolescents after the intervention whereas not in group B. Although the fecal energy only reflect part of the energy supply that could be due to the colonic microbiota, the obtained results could partly explain the detected differences in weight loss between the two adolescent groups (A and B) paralleled to microbiota changes. In fact, differences in total fecal energy between both adolescent groups could not be related to differences in total dietary energy intake. Therefore, these and previous results point for a role of gut bacteria other than common probiotic genera in weight management and, therefore, the current dietary strategies used to modulate the gut microbiota based on the administration of lactobacilli, bifidobacteria and prebiotics that favour their predominance could be questioned in obesity control.
Nevertheless, a recent study has not found correlations between Bacteroides populations and obesity by comparing obese and non-obese participants and there was no significant relationship between changes in the percentage of Bacteroides in feces and weight loss under reduced calorie diets.13 Significant shifts detected in Roseburia-E. rectale groups detected with the probe Rrec482 and Bifidobacterium were related to reductions in carbohydrate intake but regardless weight loss.13 By contrast, the present study has not shown significant reductions in these bacterial groups in feces of participants submitted to energy intake reduction, including carbohydrate restrictions (approximately 28% reduction), but confirmed previous relations between Clostridium and Bacteroides groups with weight loss under dietary intervention.10 Therefore, the possibility that weight loss depends on both the diet and its interactions with gut microbiota could not be completely disregarded.
Glucose and LDL-cholesterol reductions detected in group A of adolescents were also correlated with shifts in total Gram-negative bacteria and C. lituseburense. Although further studies should be carried out to confirm these trends, the results also suggest interactions between diet, gut bacteria and host's metabolism as proposed earlier.10
Obesity and related disorders, such as the metabolic syndrome, are also associated with a chronic low-grade inflammation even at early ages, exemplifying the link between metabolism and immunity.4 In this study, gut microbiota has been identified as a factor stimulating host immunity to different extents depending on weight loss. Elevation of IgA-coating bacteria before the intervention programme could be an indication of low-grade inflammation triggered by the gut microbiota before intervention, as this microbiota is characterized by increased levels of opportunistic pathogens when compared with that detected after the diet in group A1 of adolescents.38 The decrease in IgA-coating bacteria detected after the intervention was particularly associated with reductions in C. histolyticum and E. rectale-C. coccoides proportions. The reductions in butyrate-producing bacteria of E. rectale-C. coccoides group, as a result of the intervention programme, could also be responsible for reducing energy availability for immune cells leading to reducing IgA-producing cells and mucosal IgA concentrations.39, 40 Our results suggest that the increased host immune response trigger by the gut microbiota can be modified in overweight adolescents by a lifestyle intervention, confirming that there is a relationship between gut microbiota and host immunity in obese human participants.
The limitations of the study include the relative small sample size of subgroups because of differences in weight loss responses of the whole population group and the short duration of the intervention, which could reduce the significance of the detected changes. However, this short-term study confirmed and complemented the results of a previous long-term study (1 year) by using different molecular techniques that target specific bacterial groups.
All in all, this study has provided stronger links between specific bacterial groups and body weight in adolescents under a lifestyle intervention. This study also suggest a role for the gut microbiota in this disorder related to both host metabolism and immunity, evidenced by shifts in the bacteria driving the main metabolic pathways in the colon and showing different pathogenic features, although direct evidence should still be provided.
Conflict of interest
None of the authors has any conflict of interest.
Cole TJ, Belizzy MC, Flegal KM, Dietz WH . Establishing a standard definition for child overweight and obesity worldwide: international survey. Br Med J 2000; 320: 1–6.
Moreno LA, Mesana MI, González-Gross M, Gil CM, Fleta J, Wärnberg J et al. Bueno M and the AVENA Study Group. Anthropometric body fat composition reference values in Spanish adolescents. The AVENA Study. Eur J Clin Nutr 2006; 60: 191–196.
Badman MK, Flier JS . The adipocyte as an active participant in energy balance and metabolism. Gastroenterology 2007; 132: 2103–2115.
Wärnberg J, Nova E, Romeo J, Moreno LA, Sjöström M, Marcos A . Lifestyle-related determinants of inflammation in adolescence. Br J Nutr 2007; 98: 116–120.
Moreno LA, Ochoa MC, Wärnberg J, Marti A, Martínez JA, Marcos A . Treatment of obesity in children and adolescents. How nutrition can work? Int J Pediatr Obes 2008; 3: 72.
Bäckhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A, Semenkovich CF, Gordon JI . The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci USA 2004; 101: 15718–15723.
Wabitsch M . Overweight and obesity in European children and adolescents: causes and consequences, treatment and prevention. An introduction. Eur J Pediatr 2000; 159: 5–7.
Moreno LA, Rodríguez G . Dietary risk factors for development of childhood obesity. Curr Opin Clin Nutr Metab Care 2007; 10: 336–341.
Duncan SH, Belenguer A, Holtrop G, Johnstone AM, Flint HJ, Lobley GE . 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.
Ley RE, Turnbaugh PJ, Klein S, Gordon JI . Microbial ecology: human gut microbes associated with obesity. Nature 2006; 444: 1022–1023.
De Graaf AA, Venema K . Gaining insight into microbial physiology in the large intestine: a special role for stable isotopes. Adv Microb Physiol 2008; 53: 73–168.
Cani PD, Neyrinck AM, Fava F, Knauf C, Burcelin RG, Tuohy KM et al. Selective increases of bifidobacteria in gut microflora improve high-fat-diet-induced diabetes in mice through a mechanism associated with endotoxaemia. Diabetologia 2007; 50: 2374–2383.
Duncan SH, Lobley GE, Holtrop G, Ince J, Johnstone AM, Louis P et al. Human colonic microbiota associated with diet, obesity and weight loss. Int J Obes (Lond) 2008; 32: 1720–1724.
Matsumoto M, Inoue R, Tsukahara T, Ushida K, Chiji H, Matsubara N et al. Voluntary running exercise alters microbiota composition and increases n-butyrate concentration in the rat cecum. Biosci Biotechnol Biochem 2008; 72: 572–576.
Rodriguez G, Moreno LA, Sarría A, Fleta J, Bueno M . Resting energy expenditure in children and adolescents: agreement between calorimetry and prediction equations. Clin Nutr 2002; 21: 255–260.
Farran A, Zamora R, Cervera P, CESNID. Tablas de composición de alimentos CESNID, 2a edn. Edicions Universitat de Barcelona—McGraw-Hill/Interamericana: Barcelona, 2004.
Nadal I, Donat E, Riber-Koninckx C, Calabuig M, Sanz Y . Imbalance in the composition of the duodenal microbiota of children with coeliac disease. J Med Microbiol 2007; 56: 1669–1674.
Amann RI, Binder BJ, Olson RJ, Chisholm SW, Devereux R, Stahl DA . Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl Environ Microbiol 1990; 56: 1919–1925.
Wallner G, Amann R, Beisker W . Optimizing fluorescent in situ hybridization with rRNA-targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytometry 1993; 14: 136–143.
Langendijk PS, Schut F, Jansen GJ, Raangs GC, Kamphuis GR, Wilkinson MH et al. Quantitative fluorescence in situ hybridization of Bifidobacterium spp. with genus-specific 16S rRNA-targeted probes and its application in fecal samples. Appl Environ Microbiol 1995; 61: 3069–3075.
Franks AH, Harmsen HJ, Raangs GC, Jansen GJ, Schut F, Welling GW . Variations of bacterial populations in human feces measured by fluorescent in situ hybridization with group-specific 16S rRNA-targeted oligonucleotide probes. Appl Environ Microbiol 1998; 64: 3336–3345.
Harmsen JHM, Elfferich P, Schut F, Welling GW . A 16S rRNA Targeted probe for detection of lactobacilli and enterococci in faecal samples by fluorescent in situ hybridisation. Micriol Ecol Health Dis 1999; 11: 3–12.
Manz W, Amann R, Ludwid W, Vancanneyt M, Schleifer KH . Application of a suite of 16S rRNA-specific oligonucleotide probes designed to investigate bacteria of the phylum cytophaga-flavobacter-bacteroides in the natural environment. Microbiology 1996; 142: 1097–1106.
Sghir A, Gramet G, Suau A, Rochet V, Pochart P, Dore J . Quantification of bacterial groups within human fecal flora by oligonucleotide probe hybridisation. Appl Environ Microbiol 2000; 66: 2263–2266.
Poulsen LK, Lan F, Kristensen CS, Hobolth P, Molin S, Krogfelt KA . Spatial distribution of Escherichia coli in the mouse large intestine inferred from rRNA in situ hybridization. Infect Immun 1994; 62: 5191–5194.
Aminov RI, Walker AW, Duncan SH, Harmsen HJ, Welling GW, Flint HJ . Molecular diversity, cultivation, and improved detection by fluorescent in situ hybridization of a dominant group of human gut bacteria related to Roseburia spp. or Eubacterium rectale. Appl Environ Microbiol 2006; 72: 6371–6376.
Bullock NR, Booth JC, Gibson GR . Comparative composition of bacteria in the human intestinal microflora during remission and active ulcerative colitis. Curr Issues Intest Microbiol 2004; 5: 59–64.
Van der Waaij LA, Kroese FG, Visser A, Nelis GF, Westerveld BD, Jansen PL et al. Immunoglobulin coating of faecal bacteria in inflammatory bowel disease. Eur J Gastroenterol Hepatol 2004; 16: 669–674.
EGRAN. Technical note: Attempts to harmonize chemical analyses of feeds and faeces from rabbit feed evaluation. World Rabbit Science 2001; 9: 57–64.
Flint HJ, Duncan SH, Scott KP, Louis P . Interactions and competition within the microbial community of the human colon:links between diet and health. Environ Microbiol 2007; 9: 1101–1111.
Roediger WE . Role of anaerobic bacteria in the metabolic welfare of the colonic mucosa in man. Gut 1980; 21: 793–798.
Xu J, Bjursell MK, Himrod J, Deng S, Carmichael LK, Chiang HC et al. A genomic view of the human-Bacteroides thetaiotaomicron symbiosis. Science 2003; 299: 2074–2076.
Hamer HM, Jonkers D, Venema K, Vanhoutvin S, Troost FJ, Brummer RJ . Review article: the role of butyrate on colonic function. Aliment Pharmacol Ther 2008; 27: 104–119.
Collins MD, Lawson PA, Willems A, Cordoba JJ, Fernandez-Garayzabal J, Garcia P et al. The phylogeny of the genus Clostridium: proposal of five new genera and eleven new species combinations. Int J Syst Bacteriol 1994; 44: 812–826.
Pouteau E, Nguyen P, Ballèvre O, Krempf M . Production rates and metabolism of short-chain fatty acids in the colon and whole body using stable isotopes. Proc Nutr Soc 2003; 62: 87–93.
Wolever TM, Spadafora PJ, Cunnane SC, Pencharz PB . Propionate inhibits incorporation of colonic [1,2-13C]acetate into plasma lipids in humans. Am J Clin Nutr 1995; 61: 1241–1247.
Walker AW, Duncan SH, McWilliam Leitch EC, Child MW, Flint HJ . Ph and peptide supply can radically alter bacterial populations and short-chain fatty acid ratios within microbial communities from the human colon. Appl Environ Microbiol 2005; 71: 3692–3700.
Duncan SH, Scott KP, Ramsay AG, Harmsen HJ, Welling GW, Stewart CS et al. Effects of alternative dietary substrates on competition between human colonic bacteria in an anaerobic fermentor system. Appl Environ Microbiol 2003; 69: 1136–1142.
Marschan E, Kuitunen M, Kukkonen K, Poussa T, Sarnesto A, Haahtela T et al. Probiotics in infancy induce protective immune profiles that are characteristic for chronic low-grade inflammation. Clin Exp Allergy 2008; 38: 611–618.
Peuranen S, Tiihonen K, Apajalahti J, Kettunen A, Saarinen M, Rautonen N . Combination of polydextrose and lactitol affects microbial ecosystem and immune responses in rat gastrointestinal tract. Br J Nutr 2004; 91: 905–914.
Declaration of personal and funding interests: This work was supported by Grants AGL2005-05788-C02-01 from the Spanish Ministry of Science and Innovation and AP 002/07 from Consejería de Sanidad (Valencia, Spain). The EVASYON study was supported by grants from Spanish Ministry of Health (PI051574). The scholarship to I. Nadal from Generalidad Valenciana (Spain) and CSIC 200570F0091 (Spain) is fully acknowledged. The scholarship from CONACYT (México) to A Santacruz is fully acknowledged.
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Nadal, I., Santacruz, A., Marcos, A. et al. Shifts in clostridia, bacteroides and immunoglobulin-coating fecal bacteria associated with weight loss in obese adolescents. Int J Obes 33, 758–767 (2009). https://doi.org/10.1038/ijo.2008.260
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