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Male-specific Association Between Fat-Free Mass Index and Fecal Microbiota in 2- to 3-Year-Old Australian Children

Smith-Brown, Paula*; Morrison, Mark; Krause, Lutz; Davies, Peter S.W.*

Journal of Pediatric Gastroenterology and Nutrition: January 2018 - Volume 66 - Issue 1 - p 147–151
doi: 10.1097/MPG.0000000000001780
Original Articles: Nutrition
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Objectives: Maturation of the gut microbiota has been shown to influence childhood growth, whereas alterations in microbiota composition are proposed to be causally related to the development of overweight and obesity. The objective of this study is to explore the association between microbiota profile, body size, and body composition in young children.

Methods: Fecal microbiota was examined by 16S rRNA gene sequencing, whereas body composition was assessed using the deuterium oxide dilution technique in a cohort of 37 well-nourished 2- to 3-year-old Australian children.

Results: Microbiota composition (weighted UniFrac distance) was shown to be significantly associated with FFMI (fat-free mass index) z score (P = 0.027, adonis) in boys but not girls. In boys, FFMI z score was significantly correlated with the relative abundance of an OTU (Operational Taxonomic Unit) belonging to the Ruminococcaceae family (Rho = 0.822, P < 0.001, pFDR (false discovery rate adjusted P value) = 0.002, n = 18). At a FDR <0.2, FFMI z score in boys was positively associated with the relative abundance of OTU related to Dorea formicigenerans and Faecalibacterium prausnitzii and negatively correlated to an OTU related to Bacteroides cellulosilyticus.

Conclusions: These results suggest that previously reported associations between microbiota composition and body size may be driven by an association with fat-free mass, particularly in males.

*Children's Nutrition Research Centre, Child Health Research Centre

The University of Queensland Diamantina Institute, Translational Research Institute, The University of Queensland, Queensland, Australia.

Address correspondence and reprint requests to Paula Smith-Brown, BSc APD, Children's Nutrition Research Centre, Level 6, Child Health Research Centre, The University of Queensland, St Lucia, QLD 4067, Australia (e-mail: p.brown1@uq.edu.au).

Received 25 March, 2017

Accepted 20 September, 2017

Supplemental digital content is available for this article. Direct URL citations appear in the printed text, and links to the digital files are provided in the HTML text of this article on the journal's Web site (www.jpgn.org).

The Translational Research Institute (TRI) is supported by a grant from the Australian Government. Danone Nutricia, Australia funded a PhD living allowance stipend for P.S.B. and rRNA gene sequencing costs.

The authors report no conflicts of interest.

All raw data and related metadata underlying the findings reported in this manuscript are deposited in the Qiita (qiita.ucsd.edu) public repository (EBI accession number ERP022673).

What Is Known

  • Microbiota composition is established in the first few years of life.
  • Overweight and obese phenotypes are associated with altered microbiota profiles in both animal and human studies.
  • Maturation of the microbiota is related to both linear and weight growth in early life.

What Is New

  • Fecal microbiota profile of all the children was significantly associated with body size and body composition.
  • Sex-specific analyses revealed a significant association between microbiota composition and fat-free mass in boys but not girls.
  • In boys, fat-free mass was significantly correlated with the relative abundance of an OTU belonging to the Ruminococcaceae family.

Overweight and obese phenotypes have been shown to be associated with altered microbiota profiles in both animal and human studies (1) with the phenotype shown to be partially transmissible through colonization of germ-free mice, particularly combined with obesogenic diets (2). Meta-analysis was, however, unable to identify consistent differences between the microbiota of lean and obese humans (3) and studies which have more directly assessed adiposity have failed to find an association with microbiota composition (4). Recently, maturation of the microbiota has been shown to be related to both linear and weight growth in early life (5–7) with evidence to suggest that the microbiota interacts with host energy and protein metabolism (8,9) and the somatotropic axis (10).

The period from conception to age 24 months has been identified as the critical window of opportunity in which good nutrition and healthy growth have lasting benefits throughout life and into subsequent generations (11,12). The assessment of growth during this period is, however, largely based on anthropometric measurements of body size with insufficient attention given to the quality or composition of growth (13).

To explore the relationship between fecal microbiota profile and body composition this study uses previously reported data on fecal 16S rRNA gene sequencing (14,15). In previous publications we reported that the activity of the maternal secretor gene was associated with children's microbiota profiles at 2 to 3 years of age, particularly in children who had been exclusively breast-fed for at least 4 months (15). Our analyses further showed that in the same children, dietary intake of dairy- and plant- (soy, pulse, nuts, fruit, and vegetables) based foods were associated with microbiota composition (14). Although such findings collectively emphasize how dietary intake in early life may influence microbiota development, further exploration is needed to elucidate the effects of these microbial differences on host phenotype.

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METHODS

The present study uses previously reported fecal 16S rRNA gene sequencing data (14,15) for 37 children recruited from the ongoing Feeding Queensland Babies Study (16). In summary, fecal samples were collected from a disposable bed pan (or nappy if not toilet trained) at the participant's homes within 24 hours of the study visit and frozen immediately at −20°C. The frozen samples were transported in insulated bags with frozen ice blocks before being transferred to −80°C for storage. Fecal DNA extractions, polymerase chain reaction (PCR) amplification and library construction for bar-coded 16S rRNA gene amplicon sequencing, using the Illumina Mi-Seq platform, was performed following standard operating protocols by the Australian Centre for Ecogenomics, The University of Queensland, Australia (ecogenomic.org).

At 2 to 3 years of age, weight was measured to the nearest 0.05 kg using Tanita BWB-600 Wedderburn Scales, whereas height was measured to nearest 0.1 cm using a Seca (mod 240) wall mounted stadiometer. Waist circumference was measured using the protocol recommended by the World Health Organization (17). Waist circumference to height ratio was calculated by dividing waist circumference by height, both in centimeters. Total body water was estimated using the deuterium oxide dilution technique (18) and converted to fat-free mass using age- and sex-specific hydration constants (19). Fat mass was calculated as the difference between weight and fat-free mass. Fat mass index (FMI) and fat-free mass index (FFMI) were calculated by dividing fat-free/fat mass in kilograms by the square of height in meters (20). Body mass index (BMI) z scores were calculated using World Health Organization BMI-for-age reference data (21). Waist circumference to height ratio, FFMI, and FMI z scores were calculated using the LMS method (22) using values obtained from the NHANES datasets (23). Age- and sex-specific waist circumference, weight, height, tricep, and subscapular skinfold data for children of a healthy BMI (24) of nonhispanic white race were extracted from the 1999 to 2010 NHANES datasets. Tricep and subscapular skinfold data were converted to percentage body fat using Slaughter equations (25), which were subsequently converted to FMI and FFMI.

Delivery mode (vaginal vs cesarean section), mother's education level (university degree vs other post-school qualification) and antibiotic use in the previous 6 months (yes/no) were recorded for each child.

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Statistics

QIIME 1.9.0 (26) was used for fecal microbiota data analysis, as previously described (14,15). QIIME's pick_open_reference_otus.py workflow was used to generate OTUs using default parameters (97% sequence similarity; Greengenes reference database—version 13 8 (27); uclust OTU picking method (28)). The resulting OTU table was filtered to remove any OTUs with an abundance of <0.05% across all samples. OTUs of significance that were not initially taxonomically classified were aligned with reference sequences using SINA (SILVA Incremental Aligner) (29) to provide further identification.

The OTU table was rarefied to the minimum sample count (42,629 reads) for calculation of measures of diversity. Species richness was estimated from the rarefied OTU table using Chao1 (30), whereas Shannon index was used to estimate diversity. Beta diversity was calculated using weighted and unweighted UniFrac distances (31). Adonis (32) was employed to associate the UniFrac distances with metadata variables. Spearman rank correlation was used to explore the correlation between body composition and the relative (read count divided by total reads for that sample) abundance of taxa at the phylum, genus, and OTU level, whereas Pearson correlation (SPSS, IBM Corp Version 23, Armonk, NY.) was used to explore the correlations between body composition variables. Normality of variables was confirmed using the Kolmogorov-Smirnov test. P values were adjusted for multiple testing using the false discovery rate (FDR) Benjamini-Hochberg procedure (33).

The online Calypso platform (Version 6.4 www.cgenome.net/calypso) (34) was used to undertake nonmetric multidimensional scaling (NMDS) (35) and Pearson correlation network analysis. The absolute abundance of OTUs were normalized and transformed using default parameters (total sum normalization to convert raw counts to relative abundance, cumulative-sum scaling (36) to correct bias introduced by total sum normalization and log2 transformation to account for the non-normal distribution of taxonomic count data). Pearson correlation network analysis was performed for the 100 most abundant OTUs using a 0.4 correlation cut-off, to explore co-occurring and mutually exclusive OTU.

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Ethics

The present study was approved by The University of Queensland Medical Research Ethics Committee (Approval Number: 2012001155) and the Metro South Hospital and Health Service Human Research Ethics Committee (HREC Ref: HREC/12/QPAH457) in Brisbane, Australia and conducted in accordance with the principles expressed in the Declaration of Helsinki. All participants were provided with written and verbal information and consent forms were signed by the mothers or a legal guardian.

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RESULTS

Participant Characteristics

One of the 37 children studied was born significantly preterm at 196 days (28 weeks) and was excluded from subsequent analysis. The characteristics of the remaining 36 participants(female = 16) aged 2.24 to 3.13 (mean: 2.65) and the associations with body composition are shown in Table 1 with the associations with microbiota composition having been previously published (14). Data on body composition were available for 32 participants. Children's FFMI z score was significantly different by mother's education level (P = 0.038, t test university degree vs other post-school qualification) and child delivery mode (P = 0.009, t test vaginal vs cesarean section), whereas children's FMI z score was significantly different by sex (P = 0.010, t test) with boys having a lower mean FMI z score (M = 0.29, standard deviation [SD] = 1.28, n = 18) compared to girls (M = 1.59, SD = 1.39, n = 14). Neither mother's education level, nor delivery mode, nor sex were significantly associated with children's microbiota composition (P > 0.05) (14).

TABLE 1

TABLE 1

As expected, children's BMI z score was positively correlated with all measures of body composition (FFMI and FMI z scores) (Supplemental Table 1, Supplemental Digital Content 1, http://links.lww.com/MPG/B213). Because of children's FMI z score differing by sex (Table 1) sex-specific analysis was undertaken. In boys BMI z score was only significantly correlated with FFMI z score (r = 0.76, P < 0.001), whereas in girls BMI z score was only significantly correlated with FMI z score (r = 0.67, P = 0.009), highlighting sexual dimorphism in body composition from a young age.

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Microbiota Composition, Body Size, and Body Composition

Although the initial analysis of the fecal microbiota profiles (weighted UniFrac distances) of all the children were significantly associated with FFMI and BMI z scores (Table 2), sex-specific analyses revealed that these results were attributable principally to boys: with a significant association between microbiota composition and FFMI z score in boys (weighted UniFrac distances, R2 = 0.148, P = 0.027), but not girls (P = 0.553). Consistent with these observations, the NMDS analysis also showed clustering of fecal microbial communities by FFMI z score for boys (Fig. 1) but not girls (Supplemental Figure 1, Supplemental Digital Content 2, http://links.lww.com/MPG/B214). Among boys, FFMI z score was also positively associated with microbial richness (Chao1), although after FDR correction the association was no longer statistically significant (r = 0.52, P = 0.026, pFDR = 0.104, Supplemental Table 2, Supplemental Digital Content 3, http://links.lww.com/MPG/B215).

TABLE 2

TABLE 2

FIGURE 1

FIGURE 1

Network analysis of OTUs in boys resulted in 2 clearly distinct clusters of co-occurring bacteria, which showed a striking association with body composition (Fig. 2). One “fat” cluster represented OTUs associated with FMI z score, whereas the second “lean” cluster represented OTUs associated with FFMI and BMI z scores. One OTU (Greengenes ID: 591285) identified as Bifidobacterium longum was located between both clusters and correlated with both FMI and BMI z scores. In accordance with our Adonis and NMDS results, these same OTUs did not show any clustering by sex (Supplemental Figure 2, Supplemental Digital Content 4, http://links.lww.com/MPG/B216) or body composition for girls (Supplemental Figure 3, Supplemental Digital Content 5, http://links.lww.com/MPG/B217).

FIGURE 2

FIGURE 2

In boys, FFMI z score was significantly correlated with the relative abundance of an OTU (Greengenes ID: 1110378) belonging to the family Ruminococcaceae (Rho = 0.822, P < 0.001, pFDR = 0.002, n = 18; Supplemental Figure 4, Supplemental Digital Content 6, http://links.lww.com/MPG/B218). Colonization with this OTU (presence/absence) was associated with an increase in FFMI z score of 1.5 (P = 0.001, pFDR = 0.002) and an increase in BMI z score of 1.2 (P = 0.001, pFDR = 0.002; Supplemental Figure 5, Supplemental Digital Content 7, http://links.lww.com/MPG/B219). BMI z score also showed a trend for a positive association with the relative abundance of the genus Bifidobacterium (Rho = 0.64, P = 0.002, pFDR = 0.063, n = 20).

With a less stringent cut-off (FDR < 0.2), FFMI z score was positively associated with the relative abundance of 7 OTUs assigned to the order Clostridiales in boys (Excel Data File, Supplemental Digital Content 8, http://links.lww.com/MPG/B220). These OTUs include an OTU (Greengenes ID: 1076587) which showed a 99.9% similarity to reference sequences for Dorea formicigenerans using SINA (Rho = 0.69, P = 0.001, pFDR = 0.133), and an OTU (Greengenes ID: 365717), assigned to the species Faecalibacterium prausnitzii (Rho = 0.61, P = 0.006, pFDR = 0.173). FFMI z score in boys was negatively associated with the relative abundance of an OTU (Greengenes ID: 364179) which showed a 99.9% sequence similarity with a reference sequence for Bacteroides cellulosilyticus, using SINA (Rho = −0.61, P = 0.006, pFDR = 0.173).

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DISCUSSION

In summary, our results suggest that there is a sex-specific relationship between the faecal microbiota of 2- to 3-year-old well-nourished children and their body composition. Our initial analysis suggested that the fecal microbiota profiles of the entire cohort were significantly associated with FFMI and BMI z scores, but stratifying these data with respect to sex suggested these gross associations were principally driven by a male-specific association between fecal microbiota profile and FFMI z score. Moreover, our network analysis in boys further identified 2 specific clusters of bacteria that appear to be linked with either FFMI or FMI z scores. Further support for a sex-specific association between microbiota composition and growth is provided by reports of antibiotic exposure in early life being associated with increased childhood BMI in boys but not girls (37,38). In addition, treatment of mice with low-dose penicillin has been reported to increase weight and lean mass in male, but not female mice (39).

Sex-based differences in the gut microbiota have been reported in adults, with men shown to have significantly higher abundance of F prausnitzii compared to women (40). Among our cohort of young children, microbiota profiles were, however, not found to significantly differ by sex. Sexual dimorphism in body composition was seen in our cohort, with BMI z score associated with FFMI z score in boys and FMI z score in girls. Although FFMI and BMI z scores were not significantly different by sex, FMI z score was found to vary by sex with girls in our cohort having a higher mean FMI z score compared to boys. This could be a reflection of a limitation of this study in that fat-free mass and fat mass were estimated using the deuterium oxide dilution technique, whereas NHANES skinfold thickness data were used as the reference to create z scores, due to the lack of alternative more appropriate body composition references for 2- to 5-year-old children (20).

The FFMI z score of boys was significantly correlated with the relative abundance of an OTU assigned to the family Ruminococcaceae. A study of 1313 mostly female adult twins identified 97 OTUs that were significantly associated with visceral fat mass (41). Of these, 49 OTUs were assigned to the family Ruminococcaceae and were negatively associated with visceral fat mass (41) further supporting the association between members of the Ruminococcaceae family and healthy body composition.

Our study has also shown that FFMI z score of well-nourished 2- to 3-year-old boys was positively associated with OTUs related to D formicigenerans and F prausnitzii and negatively associated with an OTU related to B cellulosilyticus. These results are consistent with comparable studies using small animal models. Members of B cellulosilyticus have been identified as the most successful invaders of the obese microbiome during the cohousing of mice transplanted with fecal samples from female adult twin pairs discordant for obesity, resulting in the transmission of the obese phenotype being prevented (42). Blanton et al (8) recently demonstrated that gut microbiota composition can directly affect host growth pattern by inoculating actively growing mice with stool samples collected from Malawian infants with either healthy or growth faltering phenotypes. The mice receiving stool transplants from the latter group and also fed a nutrient-depleted diet showed reduced growth in total weight, lean mass, and bone, but not fat mass, despite no difference between groups in food consumption. Furthermore, both D formicigenerans and F prausnitzii were identified as “lean mass gain discriminatory” in both animal and human random forest models of gut microbiome data and were significantly correlated to serum amino acid levels.

Skeletal muscle plays a central role in metabolic health (43) with the period from conception to age 24 months identified as the critical window of opportunity in which healthy grow has lasting benefits throughout life (14). As such, our findings raise interesting questions with respect to whether and how the specific bacterial species identified here may influence host growth and body composition via the modulation of amino acid balance, host protein synthesis, and/or lean mass accretion. In addition, the recognized capabilities of some members of these species to produce “anti-inflammatory” or immunomodulatory factors affecting gut homeostasis may play a role (44–46). Understanding the interaction between microbiota, diet and growth could provide insights on how to optimize body composition and therefore metabolic health throughout life and warrants further investigation.

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REFERENCES

1. Scheepers LE, Penders J, Mbakwa CA, et al. The intestinal microbiota composition and weight development in children: the KOALA Birth Cohort Study. Int J Obes (Lond) 2015; 39:16–25.
2. Duca FA, Sakar Y, Lepage P, et al. Replication of obesity and associated signaling pathways through transfer of microbiota from obese-prone rats. Diabetes 2014; 63:1624–1636.
3. Walters WA, Xu Z, Knight R. Meta-analyses of human gut microbes associated with obesity and IBD. FEBS Lett 2014; 588:4223–4233.
4. Bergstrom A, Skov TH, Bahl MI, et al. Establishment of intestinal microbiota during early life: a longitudinal, explorative study of a large cohort of Danish infants. Appl Environ Microbiol 2014; 80:2889–2900.
5. Subramanian S, Huq S, Yatsunenko T, et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 2014; 510:417–421.
6. Dogra S, Sakwinska O, Soh SE, et al. Dynamics of infant gut microbiota are influenced by delivery mode and gestational duration and are associated with subsequent adiposity. MBio 2015; 6:pii: e02419-14.
7. Gough EK, Stephens DA, Moodie EE, et al. Linear growth faltering in infants is associated with Acidaminococcus sp. and community-level changes in the gut microbiota. Microbiome 2015; 3:24.
8. Blanton LV, Charbonneau MR, Salih T, et al. Gut bacteria that prevent growth impairments transmitted by microbiota from malnourished children. Science 2016; 351:pii: aad3311.
9. Smith MI, Yatsunenko T, Manary MJ, et al. Gut microbiomes of Malawian twin pairs discordant for kwashiorkor. Science 2013; 339:548–554.
10. Schwarzer M, Makki K, Storelli G, et al. Lactobacillus plantarum strain maintains growth of infant mice during chronic undernutrition. Science 2016; 351:854–857.
11. Bryce J, Coitinho D, Darnton-Hill I, et al. Maternal and child undernutrition: effective action at national level. Lancet 2008; 371:510–526.
12. Davies PS, Funder J, Palmer DJ, et al. Early life nutrition and the opportunity to influence long-term health: an Australasian perspective. J Dev Orig Health Dis 2016; 7:440–448.
13. International Atomic Energy Agency. Body Composition Assessment From Birth to Two Years of Age. IAEA Human Health Series No. 22, IAEA, Vienna; 2013.
14. Smith-Brown P, Morrison M, Krause L, et al. Dairy and plant based food intakes are associated with altered faecal microbiota in 2 to 3 year old Australian children. Sci Rep 2016; 6:32385.
15. Smith-Brown P, Morrison M, Krause L, et al. Mothers secretor status affects development of childrens microbiota composition and function: a pilot study. PLoS One 2016; 11:e0161211.
16. Newby R, Brodribb W, Ware RS, et al. Infant feeding knowledge, attitudes, and beliefs predict antenatal intention among first-time mothers in Queensland. Breastfeed Med 2014; 9:266–272.
17. World Health Organisation. Waist circumference and waist-hip ratio. Report of a WHO expert consultation. December 9–11, 2008. WHO. Geneva. 2008.
18. Davies PS, Wells JC. Calculation of total body water in infancy. Eur J Clin Nutr 1994; 48:490–495.
19. Fomon SJ, Haschke F, Ziegler EE, et al. Body composition of reference children from birth to age 10 years. Am J Clin Nutr 1982; 35 (5 suppl):1169–1175.
20. Wells JC. Toward body composition reference data for infants, children, and adolescents. Adv Nutr 2014; 5:320S–329S.
21. World Health Organisation Multicentre Growth Reference Study Group. WHO Child Growth Standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development. Geneva: World Health Organization. 2006.
22. Cole TJ. The LMS method for constructing normalized growth standards. Eur J Clin Nutr 1990; 44:45–60.
23. Centers for Disease Control and Prevention (CDC) National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey Data. Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. 2015. http://www.cdc.gov/nchs/nhanes.htm. Accessed October 14, 2015.
24. Cole TJ, Bellizzi MC, Flegal KM, et al. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 2000; 320:1240–1243.
25. Slaughter MH, Lohman TG, Boileau CA, et al. Skinfold equations for estimating of body fatness in children and youth. Hum Biol 1988; 60:709–723.
26. Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010; 7:335–336.
27. DeSantis TZ, Hugenholtz P, Larsen N, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 2006; 72:5069–5072.
28. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010; 26:2460–2461.
29. Pruesse E, Peplies J, Glockner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 2012; 28:1823–1829.
30. Chao A. Nonparametric estimation of the number of classes in a population. Scand J Stat 1984; 11:265–270.
31. Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 2005; 71:8228–8235.
32. Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol 2001; 26:32–46.
33. Hochberg Y, Benjamini Y. More powerful procedures for multiple significance testing. Stat Med 1990; 9:811–818.
34. Zakrzewski M, Proietti C, Ellis JJ, et al. Calypso: a user-friendly Web-server for mining and visualizing microbiome-environment interactions. Bioinformatics 2017; 33:782–783.
35. Kruskal JB. Nonmetric multidimensional scaling: a numerical method. Psychometrika 1964; 29:115–129.
36. Paulson JN, Stine OC, Bravo HC, et al. Differential abundance analysis for microbial marker-gene surveys. Nat Methods 2013; 10:1200–1202.
37. Azad MB, Bridgman SL, Becker AB, et al. Infant antibiotic exposure and the development of childhood overweight and central adiposity. Int J Obes (Lond) 2014; 38:1290–1298.
38. Ajslev TA, Andersen CS, Gamborg M, et al. Childhood overweight after establishment of the gut microbiota: the role of delivery mode, pre-pregnancy weight and early administration of antibiotics. Int J Obes (Lond) 2011; 35:522–529.
39. Cox LM, Yamanishi S, Sohn J, et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell 2014; 158:705–721.
40. De Carcer DA, Cuiv PO, Wang T, et al. Numerical ecology validates a biogeographical distribution and gender-based effect on mucosa-associated bacteria along the human colon. ISME J 2011; 5:801–809.
41. Beaumont M, Goodrich JK, Jackson MA, et al. Heritable components of the human fecal microbiome are associated with visceral fat. Genome Biol 2016; 17:189.
42. Ridaura VK, Faith JJ, Rey FE, et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 2013; 341:1241214.
43. Bayol SA, Bruce CR, Wadley GD. Growing healthy muscles to optimise metabolic health into adult life. J Dev Orig Health Dis 2014; 5:420–434.
44. Mondot S, Lepage P, Seksik P, et al. Structural robustness of the gut mucosal microbiota is associated with Crohn's disease remission after surgery. Gut 2016; 65:954–962.
45. Rossi O, Van Berkel LA, Chain F, et al. Faecalibacterium prausnitzii A2-165 has a high capacity to induce IL-10 in human and murine dendritic cells and modulates T cell responses. Sci Rep 2016; 6:18507.
46. Neff CP, Rhodes ME, Arnolds KL, et al. Diverse intestinal bacteria contain putative zwitterionic capsular polysaccharides with anti-inflammatory properties. Cell Host Microbe 2016; 20:535–547.
Keywords:

body composition; growth; obesity; pediatric

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© 2018 by European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and North American Society for Pediatric Gastroenterology,