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Persistent gut microbiota immaturity in malnourished Bangladeshi children



Therapeutic food interventions have reduced mortality in children with severe acute malnutrition (SAM), but incomplete restoration of healthy growth remains a major problem1,2. The relationships between the type of nutritional intervention, the gut microbiota, and therapeutic responses are unclear. In the current study, bacterial species whose proportional representation define a healthy gut microbiota as it assembles during the first two postnatal years were identified by applying a machine-learning-based approach to 16S ribosomal RNA data sets generated from monthly faecal samples obtained from birth onwards in a cohort of children living in an urban slum of Dhaka, Bangladesh, who exhibited consistently healthy growth. These age-discriminatory bacterial species were incorporated into a model that computes a ‘relative microbiota maturity index’ and ‘microbiota-for-age Z-score’ that compare postnatal assembly (defined here as maturation) of a child’s faecal microbiota relative to healthy children of similar chronologic age. The model was applied to twins and triplets (to test for associations of these indices with genetic and environmental factors, including diarrhoea), children with SAM enrolled in a randomized trial of two food interventions, and children with moderate acute malnutrition. Our results indicate that SAM is associated with significant relative microbiota immaturity that is only partially ameliorated following two widely used nutritional interventions. Immaturity is also evident in less severe forms of malnutrition and correlates with anthropometric measurements. Microbiota maturity indices provide a microbial measure of human postnatal development, a way of classifying malnourished states, and a parameter for judging therapeutic efficacy. More prolonged interventions with existing or new therapeutic foods and/or addition of gut microbes may be needed to achieve enduring repair of gut microbiota immaturity in childhood malnutrition and improve clinical outcomes.

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Figure 1: Bacterial taxonomic biomarkers for defining gut-microbiota maturation in healthy Bangladeshi children during the first 2 years of life.
Figure 2: Persistent immaturity of the gut microbiota in children with SAM.

Accession codes

Primary accessions

European Nucleotide Archive

Data deposits

16S rRNA sequences, generated from faecal samples in raw format prior to post-processing and data analysis, have been deposited at the European Nucleotide Archive (accession number PRJEB5482).


  1. Ahmed, T. et al. Mortality in severely malnourished children with diarrhoea and use of a standardised management protocol. Lancet 353, 1919–1922 (1999)

    Article  CAS  Google Scholar 

  2. Ashraf, H. et al. A follow-up experience of 6 months after treatment of children with severe acute malnutrition in Dhaka, Bangladesh. J. Trop. Pediatr. 58, 253–257 (2012)

    Article  Google Scholar 

  3. World Health Organization Department of Nutrition for Health and Development WHO child growth standards growth velocity based on weight, length and head circumference: methods and development. (World Health Organization, 2009)

  4. Victora, C. G., de Onis, M., Hallal, P. C., Blössner, M. & Shrimpton, R. Worldwide timing of growth faltering: revisiting implications for interventions. Pediatrics 125, e473–e480 (2010)

    Article  Google Scholar 

  5. Ahmed, T. & Begum, B. Badiuzzaman, Ali, M. & Fuchs, G. Management of severe malnutrition and diarrhea. Indian J. Pediatr. 68, 45–51 (2001)

    Article  CAS  Google Scholar 

  6. Ahmed, T. et al. P0580 Use of a standardized protocol based on local diet results in satisfactory rates of weight gain of severely malnourished children undergoing nutritional rehabilitation. J. Pediatr. Gastroenterol. Nutr. 39, S277 (2004)

    Article  Google Scholar 

  7. Lazzerini, M., Rubert, L. & Pani, P. Specially formulated foods for treating children with moderate acute malnutrition in low- and middle-income countries. Cochrane Database Syst. Rev. CD009584 (2013)

  8. Prentice, A. M. et al. Critical windows for nutritional interventions against stunting. Am. J. Clin. Nutr. 97, 911–918 (2013)

    Article  CAS  Google Scholar 

  9. Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011)

    Article  CAS  ADS  Google Scholar 

  10. Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011)

    Article  CAS  ADS  Google Scholar 

  11. Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012)

    Article  CAS  ADS  Google Scholar 

  12. Smith, M. I. et al. Gut microbiomes of Malawian twin pairs discordant for kwashiorkor. Science 339, 548–554 (2013)

    Article  CAS  ADS  Google Scholar 

  13. David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014)

    Article  CAS  ADS  Google Scholar 

  14. Tang, W. H. W. et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N. Engl. J. Med. 368, 1575–1584 (2013)

    Article  CAS  Google Scholar 

  15. Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, (2013)

  16. Breiman, L. Random Forests. Mach. Learn. 45, 5–32 (2001)

    Article  Google Scholar 

  17. Dewey, K. G. et al. Infant weight-for-length is positively associated with subsequent linear growth across four different populations. Matern. Child Nutr. 1, 11–20 (2005)

    Article  Google Scholar 

  18. Lin, A. et al. Distinct distal gut microbiome diversity and composition in healthy children from Bangladesh and the United States. PLoS ONE 8, e53838 (2013)

    Article  CAS  ADS  Google Scholar 

  19. Nahar, B. et al. Effects of a community-based approach of food and psychosocial stimulation on growth and development of severely malnourished children in Bangladesh: a randomised trial. Eur. J. Clin. Nutr. 66, 701–709 (2012)

    Article  CAS  Google Scholar 

  20. Bates, D., Maechler, M. & Bolker, B. lme4: Linear mixed-effects models using S4 classes. (2011)

  21. Mondal, D. et al. Contribution of enteric infection, altered intestinal barrier function, and maternal malnutrition to infant malnutrition in Bangladesh. Clin. Infect. Dis. 54, 185–192 (2012)

    Article  Google Scholar 

  22. Pathela, P. et al. Diarrheal illness in a cohort of children 0-2 years of age in rural Bangladesh: I. Incidence and risk factors. Acta Paediatr. 95, 430–437 (2006)

    Article  Google Scholar 

  23. Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nature Meth. 10, 57–59 (2013)

    Article  CAS  Google Scholar 

  24. Caparaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nature Meth. 7, 335–336 (2010)

    Article  Google Scholar 

  25. McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012)

    Article  CAS  Google Scholar 

  26. Liaw, A. & Weiner, M. Classification and regression by randomForest. R package version 4.6-7. R News 2, 18–22 (2002)

    Google Scholar 

  27. Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest: tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package). (2013)

  28. National Institute of Population Research and Training (NIPORT), Mitra and Associates, and ICF International. Bangladesh Demographic and Health Survey 2011. Dhaka, Bangladesh and Calverton, Maryland, USA: NIPORT, Mitra and Associates, and ICF International (2013)

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We thank the parents and children from Dhaka, Bangladesh for their participation in this study, J. Hoisington-López and S. Deng for technical assistance, and N. Griffin, A. Kau, N. Dey and J. Faith for suggestions during the course of this work. This work was supported by the Bill & Melinda Gates Foundation. The clinical component of the SAM study was funded by the International Atomic Energy Agency. The birth cohort study of singletons was supported in part by the NIH (AI043596). S.S. is a member of the Washington University Medical Scientist Training Program. A.B. is the recipient of an SBE Doctoral Dissertation Research Improvement Grant (NSF Award no. SES-1027035).

Author information

Authors and Affiliations



S.S. and J.I.G. designed the metagenomic study, S.H., T.A., R.H., M.A.A., M.M., W.A.P. Jr designed and implemented the clinical monitoring and sampling for the SAM trial, participated in patient recruitment, sample collection, sample preservation and/or clinical evaluations; S.S. generated the 16S rRNA data with assistance from M.F.M. and B.D.M.; A.B. and J.D. performed the anthropology study; S.S., T.Y., Q.Z., L.G.V., M.J.B., M.A.P. and J.I.G. analysed the data; S.S. and J.I.G. wrote the paper.

Corresponding author

Correspondence to Jeffrey I. Gordon.

Ethics declarations

Competing interests

J.I.G. is cofounder of Matatu Inc., a company that characterizes the role of diet-by-microbiota interactions in defining health. The other authors declare no competing interests.

Extended data figures and tables

Extended Data Figure 1 Illustration of the equations used to calculate ‘relative microbiota maturity’ and ‘microbiota-for-age Z-score’.

a, b, The procedure to calculate both microbiota maturation metrics are shown for a single faecal sample from a focal child (pink circle) relative to microbiota age values calculated in healthy reference controls. These reference values are computed in samples collected from children used to validate the Random-Forests-based sparse 24-taxon model and are shown in a, as a broken line of the interpolated spline fit and in b, as median ± s.d. values for each monthly chronologic age bin from months 1 to 24.

Extended Data Figure 2 Transient microbiota immaturity and reduction in diversity associated with diarrhoea in healthy twins and triplets.

a, The transient effect of diarrhoea in healthy children. Seventeen children from 10 families with healthy twins or triplets had a total of 36 diarrhoeal illnesses where faecal samples were collected. Faecal samples collected in the months immediately before and following diarrhoea in these children were examined in an analysis that included multiple environmental factors in the ‘healthy twins and triplets’ birth cohort. Linear mixed models of these specified environmental factors indicated that ‘diarrhoea’, ‘month following diarrhoea’ and ‘presence of formula in diet’ have significant effects on relative microbiota maturity, while accounting for random effects arising from within-family and within-child dependence in measurements of this maturity metric. The factors ‘postnatal age’, ‘presence or absence of solid foods’, ‘exclusive breastfeeding’, ‘enteropathogen detected by microscopy’, ‘antibiotics’ as well as ‘other periods relative to diarrhoea’ had no significant effect. The numbers of faecal samples (n) are shown in parenthesis. Mean values ± s.e.m. are plotted. *P < 0.05, ***P < 0.001. See Supplementary Table 7 for the effects of dietary and environmental covariates. b, Effect of diarrhoea and recovery on age-adjusted Shannon diversity index (SDI). Mean values of effect on SDI ± s.e.m. are plotted. *P < 0.05, **P < 0.01.

Extended Data Figure 3 Gut microbiota variation in families with twins and triplets during the first year of life.

a, Maternal influence. Heatmap of the mean relative abundances of 13 bacterial taxa (97%-identity OTUs) found to be statistically significantly enriched in the first month post-partum in the faecal microbiota of mothers (see column labelled 1) compared to microbiota sampled between the second and twelfth months post-partum (FDR-corrected P < 0.05; ANOVA of linear mixed-effects model with random by-mother intercepts). An analogous heatmap of the relative abundance of these taxa in their twin or triplet offspring is shown. Three of these 97%-identity OTUs are members of the top 24 age-discriminatory taxa (blue) and belong to the genus Bifidobacterium. b–e, comparisons of maternal, paternal and infant microbiota. Mean values ± s.e.m. of Hellinger and unweighted UniFrac distances between the faecal microbiota of family members sampled over time were computed. Samples obtained at postnatal months 1, 4, 10 and 12 from twins and triplets, mothers and fathers were analysed (n = 12 fathers; 12 mothers; 25 children). b, Intrapersonal variation in the bacterial component of the maternal microbiota is greater between the first and fourth months after childbirth than variation in fathers. c, Distances between the faecal microbiota of spouses (each mother–father pair) compared to distances between all unrelated adults (male–female pairs). The microbial signature of co-habitation is only evident 10 months following childbirth. d, e, The degree of similarity between mother and infant during the first postpartum month is significantly greater than the similarity between microbiota of fathers and infants (d) while the faecal microbiota of co-twins are significantly more similar to one another than to age-matched unrelated children during the first year of life (e). For all distance analyses, Hellinger and unweighted UniFrac distance matrices were permuted 1,000 times between the groups tested. P values represent the fraction of times permuted differences between tested groups were greater than real differences between groups. *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Figure 4 Anthropometric measures of nutritional status in children with SAM before, during and after both food interventions.

a–c, Weight-for-height Z-scores (WHZ) (a) height-for-age Z-scores (HAZ) (b) and weight-for-age Z-scores (WAZ) (c). Mean values ± s.e.m. are plotted and referenced to national average anthropometric values for children surveyed between the ages of 6 and 24 months during the 2011 Bangladeshi Demographic Health Survey (BDHS)28.

Extended Data Figure 5 Persistent reduction of diversity in the gut microbiota of children with SAM.

Age-adjusted Shannon diversity index for faecal microbiota samples collected from healthy children (n = 50), and from children with SAM at various phases of the clinical trial (mean values ± s.e.m. are plotted). The significance of differences between SDI at various stages of the clinical trial is indicated relative to healthy controls (above the bars) and versus the time of enrollment before treatment (below the bars). *P < 0.05, **P < 0.01, ***P < 0.001 (post-hoc Dunnett’s multiple comparison procedure of linear mixed models). See Supplementary Table 14.

Extended Data Figure 6 Heatmap of bacterial taxa significantly altered during the acute phase of treatment and nutritional rehabilitation in the microbiota of children with SAM compared to similar-age healthy children.

Bacterial taxa (97%-identity OTUs) significantly altered (FDR-corrected P < 0.05) in children with SAM are shown (see Supplementary Table 15 for P values and effect size for individual taxa). Three groups of bacterial taxa are shown: those enriched before the food intervention (a); those enriched during the follow-up phase compared to healthy controls (b); and those that are initially depleted but return to healthy levels (c). Members of the top 24 age-discriminatory taxa are highlighted in blue. Note that there were no children represented in the Khichuri–Halwa arm under the age of 12 months during the ‘follow-up after 3 months’ period.

Extended Data Figure 7 Heatmap of bacterial taxa altered during long-term follow-up in the faecal microbiota of children with SAM compared to similar-age healthy children.

a, b, Bacterial taxa (97%-identity OTUs) significantly altered (FDR-corrected P < 0.05) in children with SAM are shown (see Supplementary Table 15 for P values and effect sizes for individual taxa). a, Taxa depleted across all phases of SAM relative to healthy. b, Those depleted during the follow-up phase. Members of the top 24 age-discriminatory taxa are highlighted in blue. Note that there were no children under the age of 12 months represented in the Khichuri–Halwa treatment arm during the ‘follow-up after 3 months’ period.

Extended Data Figure 8 Effects of antibiotics on the microbiota of children with SAM.

Plots of microbiota and anthropometric parameters in nine children sampled before antibiotics (abx), after oral amoxicillin plus parenteral gentamicin and ampicillin, and at the end of the antibiotic and dietary interventions administered over the course of nutritional rehabilitation in the hospital. All comparisons were made relative to the pre-antibiotic sample using the non-parametric Wilcoxon matched-pairs rank test, in which each child served as his or her own control. a–c, Microbiota parameters, plotted as mean values ± s.e.m., include relative microbiota maturity, microbiota-for-age Z-score (MAZ), and SDI. WHZ scores are provided in d. e, f, The two predominant bacterial family-level taxa showing significant changes following antibiotic treatment. ns, not significant; **P < 0.01.

Extended Data Figure 9 Relative microbiota maturity and MAZ correlate with WHZ in children with MAM.

a–c, WHZ are significantly inversely correlated with relative microbiota maturity (a) and MAZ (b) in a cross-sectional analysis of 33 children at 18 months of age who were above and below the anthropometric threshold for MAM (Spearman’s Rho = 0.62 and 0.63, respectively; ***P < 0.001). In contrast, there is no significant correlation between WHZ and microbiota diversity (c). d–l, Relative abundances of age-discriminatory 97%-identity OTUs that are inputs to the Random Forests model that are significantly different in the faecal microbiota of children with MAM compared to age-matched 18-month-old healthy controls (Mann–Whitney U-test, P < 0.05). Box plots represent the upper and lower quartiles (boxes), the median (middle horizontal line), and measurements that are beyond 1.5 times the interquartile range (whiskers) and above or below the 75th and 25th percentiles, respectively (points) (Tukey’s method, PRISM software v6.0d). Taxa are presented in descending order of their importance to the Random Forests model. See Extended Data Fig. 10a, b.

Extended Data Figure 10 Cross-sectional assessment of microbiota maturity at 18 months of age in Bangladeshi children with and without MAM, plus extension of the Bangladeshi-based model of microbiota maturity to Malawi.

a, b, Children with MAM (WHZ lower than −2 s.d.; grey) have significantly lower relative microbiota maturity (a) and MAZ (b) compared to healthy individuals (blue). Mean values ± s.e.m. are plotted **P < 0.01 (Mann–Whitney U-test). See Extended Data Fig. 9 for correlations of metrics of microbiota maturation with WHZ and box-plots of age-discriminatory taxa whose relative abundances are significantly different in children with MAM relative to healthy reference controls. c, Microbiota age predictions resulting from application of the Bangladeshi 24-taxon model to 47 faecal samples (brown circles) obtained from concordant healthy Malawian twins and triplets are plotted versus the chronologic age of the Malawian donor (collection occurred in individuals ranging from 0.4 to 25.1 months old). The results show the Bangladeshi model generalizes to this population, which is also at high risk for malnutrition (each circle represents an individual faecal sample collected during the course of a previous study11). d, Spearman rho and significance of rank order correlations between the relative abundances of age-discriminatory taxa, and the chronologic age of all healthy Bangladeshi children described in the present study as well as concordant healthy Malawian twins and triplets. *P < 0.05.

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Subramanian, S., Huq, S., Yatsunenko, T. et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417–421 (2014).

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