The association of gut microbiota characteristics in Malawian infants with growth and inflammation

1 Scientific RepoRtS | (2019) 9:12893 | https://doi.org/10.1038/s41598-019-49274-y www.nature.com/scientificreports the association of gut microbiota characteristics in Malawian infants with growth and inflammation Arox W. Kamng’ona 1, Rebecca Young2, charles D. Arnold 2, emma Kortekangas3, noel patson4, Josh M. Jorgensen2, elizabeth L. prado 2, David chaima4, chikondi Malamba4, Ulla Ashorn3, Yue-Mei fan 3, Yin B. cheung5, per Ashorn3,6, Kenneth Maleta4 & Kathryn G. Dewey2 We tested the hypotheses that a more mature or diverse gut microbiota will be positively associated with infant growth and inversely associated with inflammation. We characterized gut microbiota from the stool samples of Malawian infants at 6 mo (n = 527), 12 mo (n = 632) and 18 mo (n = 629) of age. Microbiota diversity and maturity measurements were based on Shannon diversity index and microbiota for age Z-score (MAZ), respectively. Growth was calculated as change in Z-scores for weight-for-age (WAZ), length-for-age (LAZ) and head circumference-for-age (HCZ) from 6 to 12 mo and 12 to 18 mo. Biomarkers of inflammation (alpha-1-acid glycoprotein (AGP) and C-reactive protein (CRP)) were measured at 6 and 18 mo. Multivariable models were used to assess the association of each independent variable with each outcome. Microbiota diversity and maturity were related to growth in weight from 6 to 12 mo, but not to growth in length or head circumference or to growth from 12 to 18 mo. Microbiota diversity and maturity may also be linked to inflammation, but findings were inconsistent. The human gut is colonized by a vast array of microorganisms, which are largely commensal1. These microorganisms constitute what is known as the gut microbiota, and they play an important role in the development of the host immune system2 and many physiological functions that are important for the survival of the host3. Alterations to the normal gut microbial status (dysbiosis) have been associated with obesity4, kwashiorkor5 and inflammatory diseases6–8. It has been reported that inflammatory conditions such as Crohn’s disease (CD) and ulcerative colitis (UC) are related to the loss of enteric bacterial diversity9,10. A recent study in animal models demonstrated that enrichment of Enterobacteriaceae was associated with the development of CD, while depletion of this bacterial family led to a reduction in inflammation11. It has also been shown that a depletion of Faecalibacterium prausnitzii was associated with the recurrence of CD in mice with chemically induced colitis, while supplementing the mice with this bacterium led to a reduction in inflammation12. In healthy children, it is reported that there is rapid and high diversification of bacterial microbiome over the first year of life, however this diversification is delayed and lower in children with allergy and asthma13,14 or in those who are malnourished15. A study in Malawian children demonstrated an association of specific bacterial taxa with environmental enteric dysfunction (EED), a chronic condition of intestinal inflammation and blunting of intestinal villi16. Experimental work in gnotobiotic mice implanted with stool samples from Malawian infants has shown that gut microbiota composition and maturity are associated with growth status5,15,17. Furthermore, undernourished children exhibited an immature microbiota, which transmitted impaired growth phenotypes in mice models17. In the sample of children from the latter study, microbiota maturity was positively associated with anthropometric status at 18 mo of age17, but the analysis was confined to correlations with attained growth status and did not examine change in growth status over time. A study in the Gambia reported associations of the gut 1Department of Biomedical Sciences, University of Malawi, College of Medicine, Blantyre, Malawi. 2Program in International and Community Nutrition and Department of Nutrition, University of California, Davis, CA, USA. 3Center for Child Health Research, Faculty of Medicine and Health Technology, University of Tampere, Tampere, Finland. 4School of Public Health and Family Medicine, University of Malawi, College of Medicine, Blantyre, Malawi. 5Center for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore, Singapore. 6Department of Paediatrics, Tampere University Hospital, Tampere, Finland. Correspondence and requests for materials should be addressed to A.W.K. (email: [email protected]) Received: 10 February 2019 Accepted: 22 August 2019 Published: xx xx xxxx open 2 Scientific RepoRtS | (2019) 9:12893 | https://doi.org/10.1038/s41598-019-49274-y www.nature.com/scientificreports www.nature.com/scientificreports/ microbiota with infant morbidity, inflammation and growth18, but the sample size (n = 33) was too small to permit definitive conclusions. Another study reported that linear growth faltering was associated with the presence of Acidaminococcus and community-level changes in the gut microbiota19. While it is clear from these reports that the gut microbiota composition plays an influential role in inflammation and enteropathy16,20, which may be linked to growth faltering21,22, the nature of this relationship and the functional consequences of variations in the gut microbiota during infancy remain to be fully understood. We used prospective data from a large cohort of children in Malawi to investigate whether characteristics of the microbiota in infancy are associated with growth and inflammation. We tested the following hypotheses: (i) a more mature or diverse microbiota at 6 or 12 mo will be positively associated with infant growth during the subsequent six months, based on change in length for age z-score (LAZ), weight for age z-score (WAZ), weight for length z-score (WLZ), and head circumference z-score (HCZ), (ii) inverse relationships will be observed between a more mature or diverse microbiota and concurrent biomarkers of inflammation at 6 mo and 18 mo, and between a more mature or diverse microbiota at 6 mo or 12 mo and future inflammation at 18 mo. We also investigated, as a secondary objective, the association of specific bacterial taxa with infant growth, based on change in LAZ, WAZ, and WLZ. Results Study profile and follow-up outcome. Among the 869 mothers assigned to the follow-up study (Fig.1), 761 singleton live births were reported. The women who were not included experienced either spontaneous abortions/stillbirths (n = 20), dropped out of the study (n = 68) or gave birth to twins (n = 20). At 18 mo, 622 children completed anthropometric measurements and the rest (n = 138) were lost to follow-up. Data on microbiota composition of stool samples were available for 515 children at 6 mo and 630 children at 12 mo. The increase in the number of children from 6 to 12 mo was due to the higher prevalence of diarrhoea cases at 6 mo which prevented stool collection. Baseline characteristics and infant gut microbiota characteristics. At baseline, the mothers excluded from this sub study were similar to the mothers included in the study for most of the characteristics considered (Table1). However, those excluded were from households with a higher BMI, higher mean asset score and lower likelihood of severe food insecurity. The mean (SD) of MAZ was 0.64 (2.92) at 6 mo, −0.28 (2.66) at 12 mo, and −1.32 (1.76) at 18 mo. The decrease in MAZ score with age suggests a worsening relative microbiota maturity in this cohort, which parallels the worsening of height-for-age Z-scores in the same setting. The mean Figure 1. Study profile and follow-up. 3 Scientific RepoRtS | (2019) 9:12893 | https://doi.org/10.1038/s41598-019-49274-y www.nature.com/scientificreports www.nature.com/scientificreports/ (SD) of MAZ, at all time-points in the longitudinal model, was 0.08 (2.48). The mean (SD) of Shannon index was 1.61 (0.65) at 6 mo, 2.40 (0.67) at 12 mo, and 2.94 (0.62) at 18 mo. The mean (SD) of Shannon index, at all time-points in the longitudinal model, was 2.03 (0.73). Shannon index and MAZ were substantially correlated at each time-point. The Spearman’s correlation coefficients were 0.65 at 6 mo, 0.76 at 12 mo, and 0.76 at 18 mo. The Spearman’s correlation coefficient for the longitudinal association was 0.49. Association of microbiota maturity or diversity with infant growth from 6–12 or 12–18 mo. There was no significant interaction between MAZ and time regarding change in LAZ, HCZ or WLZ in either unadjusted or adjusted models (Table2). MAZ was negatively associated with change in HCZ (p < 0.0001) and positively associated with change in WLZ (p < 0.0001) from 6 to 18 mo in unadjusted models, but these associations became non-significant in adjusted models. There was an interaction between MAZ and time regarding change in WAZ; accordingly, cross-sectional models were examined, which revealed that MAZ at 6 mo was positively related to change in WAZ from 6 to 12 mo in both unadjusted and adjusted models (Table3 and Fig.2), whereas there was no significant association of MAZ at 12 mo with change in WAZ from 12 to 18 mo. The interaction between Shannon index and time was not significant for change in LAZ or HCZ in either unadjusted or adjusted models (Table4). Diversity was not associated with change in LAZ or HCZ from 6 to 18 mo in unadjusted or adjusted models. There was an interaction between Shannon index and time regarding change in WAZ and WLZ (although the latter became marginally significant in the adjusted model) (Table4); accordingly, cross-sectional models were examined, which revealed that diversity was positively related to change in WAZ (Table5 and Fig.3) and WLZ (Table5) from 6 to 12 mo in both unadjusted and adjusted models, whereas there was no significant association with change in WAZ or WLZ from 12 to 18 mo. Association of microbiota maturity or diversity with biomarkers of inflammation at 6 and 18 mo of age. There was no predictive association between MAZ at 6 mo or 12 mo and biomarkers of inflammation at 18 mo (data not shown). MAZ at 6 mo was not associated with concurrent inflammation but MAZ at 18 mo was associated with inflammation at 18 mo based on CRP concentration and high CRP (Table6). For a one Z-score unit increase in MAZ, there was a 12% decrease in CRP concentration (β = 0.88, 95% CI: 0.88 (0.80, 0.96), p = 0.003)) and the odds of high CRP decreased by 14% (OR:0.86, 95% CI: (0.78, 0.96), p = 0.009). There was a higher percentage of high CRP values at 18 mo for concurrent MAZ below median compared with MAZ above median (Fig.4). Microbial diversity at 6 mo was associated with AGP at 6 mo but not CRP (Table6): for a one-unit increase in Shannon index, there was a 5.9% increase in AGP concentration (g/L) (β = 1.06, 95% CI: (1.02, 1.10), p = 0.042). However, there was no association between microbiota diversity at 6 mo or 12 mo and biomarkers of inflammation at 18 mo (data not shown). Microbiota diversity at 18 mo was associated with CRP and high CRP at 18 mo, while no relationship was observed with AGP or high AGP. For a one-unit increase in Shannon index at 18 mo, there was a 36% decrease in CRP (β = 0.64, 95% CI: (0.55, 0.92), p = 0.01) concentration at 18 mo, and the odds of a high CRP decreased by 32% (OR = 0.68, 95% CI: (0.50, 0.93), p = 0.016). There was a higher percentage of high CRP values at 18 mo for concurrent Shannon index below median compared with Shannon index above median (p < 0.001) (Fig.4). taxa associated with infant growth. Because we found that MAZ and Shannon index at 6 mo were related to infant growth from 6 to12 mo, we examined the specific taxa at 6 mo associated with growth for the Characteristic Included Excluded p-value Participants, n 691 707 Maternal age at enrollment, years 25.2 (5.9) 24.8 (6.2) 0.20 Maternal height, cm 156.1 (5.7) 156.0 (5.6) 0.68 Maternal BMI, kg/m222.0 (2.8) 22.4 (2.9) 0.04 Maternal education completed, years 3.8 (3.5) 4.2 (3.4) 0.08 Positive malaria RDT of the mother at enrollment 22.4% 23.9% 0.52 HIV+ status of the mother 11.9% 15.5% 0.06 Mode of delivery (% with vaginal delivery) 94.9% 93.4% 0.28 Food insecure household 38.9% 33.1% 0.03 Access to sanitary facility (% with flash toilet) 9.6% 8.5% 0.57 Household asset Z-score −0.08 (1.0) 0.09 (1.0) 0.01 LAZ at 6 mo −1.3 (1.1) −1.1 (1.1) 0.08 WAZ at 6 mo −0.57 (1.2) −0.56 (1.1) 0.89 High AGP at 6 mo 64.4% — High CRP at 6 mo 28.8% — Table 1. Characteristics of included and excluded participants. Values are in mean (standard deviation) or percentages. p-values are obtained from t-test (continuous variables) or chi-square test (proportions). BMI (Body mass index). RDT (rapid diagnostic test). LAZ (Length-for-age Z-score). WAZ (Weight-for-age Z score). AGP (Alpha-1 acid glycoprotein). C-reactive protein (CRP). 4 Scientific RepoRtS | (2019) 9:12893 | https://doi.org/10.1038/s41598-019-49274-y www.nature.com/scientificreports www.nature.com/scientificreports/ 6–12 mo period. No further analysis of specific taxa at 12 mo was conducted since there was no significant association of either MAZ or Shannon index at 12 mo with change in Z scores from 12 to18 mo. At 6 mo, there were 7428 OTUs in total; following filtering steps, only 291 OTUs remained. Of these, 64% (187/291) and 60% (174/291) were associated with Shannon index and MAZ (p < 0.05), respectively. Only a few of the 291 OTUs were significantly associated with at least one of the growth outcomes (change in LAZ, WAZ or WLZ, Figs5 and 6), either positively (shown in green) or negatively (shown in red). Those shown in light green or pink were significant only before the FDR correction; those in dark green or red remained significant after the FDR correction. Generally, there were more positive associations than negative associations. In addition, there were very few associations with linear growth (change in LAZ), whereas numerous significant associations with weight (change in WAZ or WLZ) were observed. The associations with change in WAZ and WLZ generally occurred in the same direction, i.e. if a given taxon was positively associated with change in WAZ, it was also positively associated with change in WLZ, with the same trend observed for negative associations. Regarding the specific taxa at 6 mo associated with change in weight between 6 and 12 mo, we detected 8 OTUs that were negatively associated with change in WAZ. These included Actinomyces, Actinomyces graevenitzii, Atopobium parvulum (Fig.5) and Lactococcus, Streptococcus, Streptococcus mitis, and Clostridium difficile (Fig.6). We detected 20 OTUs that were positively associated with change in WAZ. These included Prevotella, Campylobacter, Enterobacteriaceae (family), Enterobacter ludwigii, Enterobacter_sp_A5_2, Klebsiella (genus), Salmonella enterica (Fig.5) and Clostridium Lactobacillus rogosae, Eubacterium hallii, Eubacterium rectale, Microbiota maturity (MAZ) Outcome (n) Unadjusted Outcome (n) Adjusted β of MAZ in model without interaction p-value β of MAZ in model without interaction p-value Interaction, age-interval * MAZ β of MAZ Interaction, age-interval * MAZ β of MAZ LAZ (1058) −0.01 (−0.02, 0.01) 0.308 0.364 LAZ (993) −0.01 (−0.02, 0.01) 0.393 0.371 HCZ (1052) −0.18 (−0.22, −0.14) 0.101 <0.0001 HCZ (998) −0.01 (−0.02, 0.00) 0.199 0.171 WLZ (1059) 0.01 (0.00, 0.03) 0.125 <0.0001 WLZ (992) 0.01 (−0.01, 0.03) 0.159 0.37 WAZ (1057) 0.019 WAZ (999) 0.039 Table 2. The longitudinal association of microbiota maturity at 6 or 12 mo with infant growth from 6 to 18 mo of age. The relationship between microbiota maturity (MAZ) and each pre-defined outcome (LAZ, lengthfor-age z score; HCZ, head-circumference z score; WLZ, weight-for-length z score; and WAZ, weight-forage z score) was tested for significance in multivariate models while controlling for other factors, including nutrition intervention group. The β of MAZ in the model without the interaction term was derived from a model including terms for the age interval and MAZ, but no interaction of age-interval and MAZ. The term ‘Age-interval *MAZ’ tested the significance of the interaction between time and microbiota maturity in a longitudinal model. The repeated measures models included two age intervals based on child’s age; in the first age interval, the response variable was change in z-score between 6 and 12 mo and the predictor was MAZ at 6 mo, and in the second age interval the response was change in z-score between 12 and 18 mo and the predictor was MAZ measured at 12 mo. The p-values were obtained from repeated measures ANCOVA. The models were adjusted for the following pre-specified covariates: intervention group; child age on day of stool collection; maternal age, height, body mass index, parity, education, HIV status, and hemoglobin at enrollment; household assets, food security, source of drinking water (tap water vs any other source), residential location and access to sanitary facility (water closet or ventilation improved pit latrine vs. none or regular pit latrine); season at time of stool sample collection; mode of delivery (vaginal or cesarean); site of delivery; and child sex. Outcome MAZ time point of measurement (n) Unadjusted Adjusted β of MAZ p of β of MAZ β of MAZ p of β of MAZ ΔWAZ (6–12 mo) 6 mo (n = 468) 0.03 (0.01, 0.05) 0.001 0.02 (0.00, 0.05) 0.033 ΔWAZ (12–18 mo) 12 mo (n = 592) −0.02 (−0.03, 0.01) 0.463 −0.01 (−0.03, 0.01) 0.35 Table 3. Association of microbiota maturity with growth in weight when the interaction between age interval and MAZ was significant. The relationship between microbiota maturity (MAZ) and change in WAZ (weightfor-age z score) when there was a significant interaction between age interval and MAZ. The relationship was assessed for both unadjusted and adjusted models. The p-values were obtained from repeated measures ANCOVA. The models were adjusted for the following pre-specified covariates: intervention group; child age on day of stool collection; maternal age, height, body mass index, parity, education, HIV status, and hemoglobin at enrollment; household assets, food security, source of drinking water (tap water vs any other source), residential location and access to sanitary facility (water closet or ventilation improved pit latrine vs. none or regular pit latrine); season at time of stool sample collection; mode of delivery (vaginal or cesarean); site of delivery; and child sex. 5 Scientific RepoRtS | (2019) 9:12893 | https://doi.org/10.1038/s41598-019-49274-y www.nature.com/scientificreports www.nature.com/scientificreports/ Faecalibacterium prausnitzii, and Ruminococcus obeum (Fig.6). There were 5 OTUs that were negatively associated with change in WLZ and 23 OTUs positively associated with change in WLZ, however these associations did not remain significant after FDR correction. Overall, we observed that Proteobacteria and Bacteroidetes were positively associated with weight growth, while Actinobacteria (except for Bifidobacterium dentium) taxa were negatively associated with weight growth (Fig.5). Regarding the specific taxa associated with growth in length, we observed 10 and 7 OTUs (Figs5 and 6) that were negatively and positively associated with change in LAZ, respectively. However, these associations did not remain significant following FDR adjustment. Taxa associated with inflammatory biomarkers. We examined taxa at 6 mo associated with AGP concentration at 6 mo because we observed a significant association of that outcome with microbiota diversity at 6 mo in the primary analyses. We detected 5 OTUs that were either negatively or positively related to AGP at 6 mo. We observed that when Slackia isoflavoniconvertens species was present at any abundance, AGP was Figure 2. Association of MAZ at 6 mo of age with change in WAZ from 6 to 12 mo of age. The figure was generated using data from adjusted cross-sectional models and shows a positive relationship between microbiota maturity (MAZ) and change in WAZ from 6 to 12 mo (p = 0.033). The positive relationship was also observed in un-adjusted models (p = 0.001). Outcome (n) Microbiota diversity (Shannon index) Unadjusted Adjusted β of Shannon index in model without interaction p-value β of Shannon index in model without interaction p-value Interaction, ageinterval * Shannon index β of Shannon index Interaction, ageinterval * Shannon index β of Shannon index LAZ (1058) −0.02 (−0.08, 0.04) 0.365 0.539 −0.03 (−0.09, 0.04) 0.485 0.397 HCZ (1052) −0.01 (−0.06, 0.05) 0.158 0.838 −0.01 (−0.06, 0.05) 0.338 0.847 WLZ (1059) 0.025 0.03 (−0.04, 0.10) 0.054 0.436 WAZ (1057) 0.006 0.037 Table 4. The longitudinal association of Shannon diversity index at 6 or 12 mo with infant growth from 6 to 18 mo of age. The relationship between microbiota diversity (Shannon index) and growth (LAZ, length-forage z score; HCZ, head-circumference z score; and WLZ, weight-for-length z score) was tested for significance in multivariate models while controlling for other factors, including nutrition intervention group. The β of Shannon index in the model without the interaction term was derived from a model including terms for the age interval and Shannon index, but no interaction of age-interval and Shannon index. The term ‘Age-interval *H’ tested the significance of the interaction between age interval and microbiota diversity in a longitudinal model. The repeated measures models included two age intervals based on child’s age; in the first age interval, the response variable was change in z-score between 6 and 12 mo and the predictor was Shannon index at 6 mo, and in the second age interval, the response was change in z-score between 12 and 18 mo and the predictor was Shannon index measured at 12 mo. The p-values were obtained from repeated measures ANCOVA. The models were adjusted for the following pre-specified covariates: intervention group; child age on day of stool collection; maternal age, height, body mass index, parity, education, HIV status, and hemoglobin at enrollment; household assets, food security, source of drinking water (tap water vs any other source), residential location and access to sanitary facility (water closet or ventilation improved pit latrine vs. none or regular pit latrine); season at time of stool sample collection; mode of delivery (vaginal or cesarean); site of delivery; and child sex. 6 Scientific RepoRtS | (2019) 9:12893 | https://doi.org/10.1038/s41598-019-49274-y www.nature.com/scientificreports www.nature.com/scientificreports/ lower compared to when the species was absent (β = −0.14 (−0.27, −0.01), p = 0.03). The presence of other taxa such as Ruminococcus gnavus (β = −0.16 (0.05, 0.27), p = 0.01), Lactobacillus (β = −0.26 (0.08, 0.45), p = 0.01), Clostridiales (β = −0.13 (0.04, 0.23), p = 0.01) and Clostridium (β = 0.1 (0.00, 0.20), p = 0.05) was positively associated with AGP at 6 mo. The relationship between Slackia isoflavoniconvertens, Ruminococcus gnavus, and Lactobacillus and AGP remained significant after FDR correction. To help understand these associations, we further examined the association of these taxa with Shannon index. We observed a positive and significant relationship of Ruminococcus gnavus (β = 0.22 (0.08, 0.35), p = 0.0018), Slackia isoflavoniconvertens (β = 0.61 (0.44, 0.78), p = 0.0001), Clostridiales (β = 0.27 (0.15, 0.39), p = 0.0001) and Clostridium (β = 0.32 (0.20, 0.45), p = 0.0001) with Shannon index at 6 mo. Each relationship remained significant after FDR correction. However, Enterobacteriaceae and Lactobacillus were not associated with Shannon index. We examined taxa at 18 mo associated with CRP concentration at 18 mo because we observed significant associations of that outcome with both microbiota maturity and diversity at 18 mo. Several taxa exhibited a negative association with CRP at 18 mo including Prevotella (β = −1 (−1.9, −0.11), p = 0.03) and Leuconostoc (β = −0.87 (−1.59, −0.16), p = 0.02), whereas Clostridium innocuum (β = 0.89 (0.18, 1.6), p = 0.01) exhibited a positive association. The relationship between Clostridium innocuum and CRP at 18 months remained significant after FDR correction. Except for Leuconostoc, which was not related to Shannon index or MAZ, Prevotella and Clostridium innocuum were positively related to Shannon index (β = 0.29 (0.16, 0.41), p < 0.0001; β = 0.19 (0.03, 0.34), p = 0.02, respectively) and MAZ (β = 0.29 (0.16, 0.41) p < 0.0001; β = 0.19 (0.03, 0.34), p = 0.02, respectively) at 18 mo. Outcome Shannon index time point of measurement Unadjusted Adjusted β of Shannon index p of β of Shannon index β of Shannon index p of β of Shannon index ΔWAZ (6–12 mo) 6 mo 0.14 (0.05, 0.25) 0.002 0.10 (0.01, 0.19) 0.023 ΔWAZ (12–18 mo) 12 mo −0.03 (−0.11, 0.05) 0.469 0.04 (−0.15, 0.05) 0.35 ΔWLZ (6–12 mo) 6 mo 0.16 (0.05, 0.28) 0.005 0.15 (0.04, 0.26) 0.01 ΔWLZ (12–18 mo) 12 mo −0.01 (−0.10, 0.09) 0.879 −0.03 (−0.13, 0.07) 0.59 Table 5. Association of microbiota diversity with growth in weight when the interaction between age interval and Shannon index was significant. The relationship between microbiota diversity (Shannon index) and change in WAZ (weight-for-age z score) and WLZ (weight-for-length z score) when there was a significant interaction between age interval and Shannon index. The relationship was assessed for both unadjusted and adjusted models. The p-values were obtained from repeated measures ANCOVA. The models were adjusted for the following pre-specified covariates: intervention group; child age on day of stool collection; maternal age, height, body mass index, parity, education, HIV status, and hemoglobin at enrollment; household assets, food security, source of drinking water (tap water vs any other source), residential location and access to sanitary facility (water closet or ventilation improved pit latrine vs. none or regular pit latrine); season at time of stool sample collection; mode of delivery (vaginal or cesarean); site of delivery; and child sex. Figure 3. Association between microbiota diversity at 6 months of age and change in WAZ from 6 to 12 mo of age. The figure was generated using data from adjusted cross-sectional models and shows a positive relationship of Shannon index with change in WAZ from 6 to 12 mo (p = 0.023). The positive relationship was also observed in un-adjusted models (p = 0.002). 7 Scientific RepoRtS | (2019) 9:12893 | https://doi.org/10.1038/s41598-019-49274-y www.nature.com/scientificreports www.nature.com/scientificreports/ Discussion We investigated whether characteristics of the gut microbiota in infancy are associated with subsequent growth and inflammation. We first tested the hypothesis that a more mature or diverse microbiota will be positively associated with infant growth. There was no association between MAZ and growth from 6 to 18 mo in relation to LAZ, HCZ or WLZ. MAZ at 6 mo was positively, though weakly, related to change in WAZ from 6 to 12 mo, while MAZ at 12 mo was not related to change in WAZ from 12 to 18 mo. We observed no association between Shannon index and change in LAZ or HCZ from 6 to 18 mo. However, there was a positive relationship of Shannon index at 6 mo with change in WAZ and WLZ from 6 to 12 mo, whereas no association of Shannon index at 12 mo with change in WAZ or WLZ from 12 to 18 mo was observed. Next, we tested the hypothesis that a more mature or diverse microbiota will be inversely related to biomarkers of inflammation (CRP and AGP). At 6 mo, microbiota maturity was not related to either biomarker, but Shannon index was positively related to AGP concentration at the same time point. At 18 mo, both microbiota maturity and Shannon index were inversely related to CRP (but not AGP) concentration at the same time point. The positive associations of both MAZ and Shannon index with growth in weight between 6 and 12 mo, but not between 12 and 18 mo, suggests that the second half of infancy is a critical period. Around 6 mo, the transition from predominant breastfeeding to a mixed diet with increasing amounts of complementary foods is underway, Predictor Variable Age for measured inflammation outcomes Inflammation outcome variables Alpha-1 acid glycoprotein (AGP)*C-reactive protein (CRP)*High AGP High CRP βvalue (95% CI) p-value β-value (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value MAZ (6 mo) 6 mo 1.01 (0.99, 1.03) 0.17 0.97 (0.90, 1.06) 0.47 1.03 (0.90, 1.09) 0.849 1.09 (0.99, 1.19) 0.072 Shannon index (6 mo) 1.06 (1.02, 1.10) 0.042 0.92 (0.86, 1.89) 0.6 1.39 (0.97, 2.00) 0.072 1.04 (0.71, 1.54) 0.831 MAZ (6 mo) 18 mo 1.00 (0.98, 1.01) 0.780 0.99 (0.92, 1.06) 0.748 1.00 (0.91, 1.09) 0.943 1.03 (0.94, 1.13) 0.501 Shannon index (6 mo) 0.97 (0.91, 1.03) 0.375 0.90 (0.66, 1.22) 0.487 1.00 (0.71, 1.41) 0.992 1.12 (0.79, 1.60) 0.530 MAZ (18 mo) 18 mo 1.01 (0.98, 1.03) 0.89 0.88 (0.80, 0.96) 0.0034 0.97 (0.88, 1.09) 0.618 0.86 (0.78, 0.96) 0.009 Shannon index (18 mo) 0.97 (0.92, 1.03) 0.22 0.64 (0.55, 0.92) 0.01 0.85 (0.62, 1.16) 0.31 0.68 (0.50, 0.93) 0.016 Table 6. The association of microbiota maturity (MAZ) or microbiota diversity (Shannon index) at 6 or 18 mo with inflammation. Inflammation was assessed by measuring Alpha-1 acid glycoprotein (AGP) and C-reactive protein (CRP) from blood/plasma as biomarkers reported as g/L and mg/L respectively. High AGP was defined as [AGP] value >1.0 g/L and high CRP as [CRP] value >5.0 mg/L. The association of MAZ and Shannon index with inflammation was assessed by controlling for child age on day of stool collection; maternal age, height, body mass index, parity, education, HIV status, and hemoglobin at enrollment; household assets, food security, source of drinking water (tap water vs any other source), residential location and access to sanitary facility (water closet or ventilation improved pit latrine vs. none or regular pit latrine); season at time of stool sample collection; mode of delivery (vaginal or cesarean); site of delivery; and child sex. There was no difference in findings from the unadjusted and adjusted models, so this table only shows adjusted models. *Beta values are back-transformed from a natural log transformation. Figure 4. Association of inflammation with MAZ and microbiota diversity. The figure shows the concurrent association of acute inflammation with MAZ and Shannon index at 18 mo of age. The light grey bars show the percentage of high CRP for values of MAZ or Shannon index below the median. The dark grey bars show the percentage of high CRP for values of MAZ or Shannon index above the median. The percentage with high CRP is higher for values of MAZ (p = 0.111) or Shannon index (p < 0.001) below the median. 8 Scientific RepoRtS | (2019) 9:12893 | https://doi.org/10.1038/s41598-019-49274-y www.nature.com/scientificreports www.nature.com/scientificreports/ which has been associated with a sudden and major shift in the gut microbiome profile23 as demonstrated in piglets24. Variations in the microbiota profile at 12 mo may be less consequential for growth than those at earlier ages, given that complementary foods are well established in the diet by that age. In a cross-sectional study among children in Bangladesh at 18 mo of age, WLZ was not correlated with Shannon index but was inversely correlated with MAZ15. The lack of association of WLZ with microbial diversity is consistent with our findings during the 12–18 mo period, but the inverse association with MAZ is in conflict with our findings. The longitudinal nature of our study, and the inclusion of data on the gut microbiota during the first year of life, provide information that is not available from previous studies, which may help to explain these contradictory findings. Regarding the specific taxa that might be related to growth from 6 to 12 mo, we found several associations that remained significant following FDR correction. Change in WAZ between 6 and 12 mo was positively associated with presence of several strains that are capable of hydrolysing cellulose such as Prevotella, Ruminococcus sp, Clostridium sp, Eubacterium sp and Bacteroides25,26. Prevotella is also thought to improve glucose metabolism through promotion of increased glycogen storage27. It is possible that the metabolic role played by Prevotella and other cellulolytic bacteria increases the availability of glucose which subsequently promotes weight growth. An unexpected finding was a significant positive association of Salmonella enterica with weight growth. Salmonella enterica is a foodborne pathogen responsible for inflammatory disease in the intestine following diarrhoea and is implicated in many deaths globally28,29. Taxa such as Actinobacteria (Actinomyces, Atopobium) and Firmicutes (Lactococcus, Streptococcus, and Clostridium) were negatively associated with change in WAZ. In contrast to our findings, a previous study in mice at weaning age showed that Firmicutes promoted weight gain, presumably because of the ability of these bacteria to digest complex sugars30,31; however, this could be driven by other genera and not necessarily Lactococcus, Streptococcus or Clostridium. In addition, metabolic effects in mice may not necessarily predict outcomes in humans. Although it has previously been reported that linear growth faltering is associated with taxa such as Acidaminococcus19, we did not find any significant association of any given taxa with linear growth in our study. With regard to the associations between the microbiota and markers of inflammation, the finding of a concurrent positive relationship between Shannon index and AGP (a long-term biomarker of inflammation) at 6 mo and not CRP (a short-term biomarker of inflammation) was unexpected. Raised AGP levels (with normal CRP levels) are normally observed in subjects who have recovered from inflammatory conditions and are convalescing32. It Bivariate LAZWAZ WLZ Proteobacteria; Salmonella; OTU4384058 Proteobacteria; Klebsiella; OTU4376230 Proteobacteria; Escherichia; OTU4375000 Proteobacteria; Enterobacter; OTU114462 Proteobacteria; Enterobacter; OTU581021 Proteobacteria; Enterobacter; OTU91557 Proteobacteria; und; OTU1010113 Proteobacteria; und; OTU688934 Proteobacteria; Campylobacter; OTU302158 Proteobacteria; Sutterella; OTU178885 Bacteroidetes; Prevotella; OTU301253 Bacteroidetes; Prevotella; OTU297414 Bacteroidetes; Prevotella; OTU180825 Bacteroidetes; Parabacteroides; OTU4365130 Bacteroidetes; Parabacteroides; OTU578016 Bacteroidetes; Parabacteroides; OTU4329571 Bacteroidetes; Bacteroides; OTU4439360 Bacteroidetes; Bacteroides; OTU4256470 Actinobacteria; Eggerthella; OTU4393532 Actinobacteria; Atopobium; OTU4451251 Actinobacteria; Bifidobacterium; OTU553611 Actinobacteria; Corynebacterium; OTU912997 Actinobacteria; Actinomyces; OTU875735 Actinobacteria; Actinomyces; OTU12574 Phylum; Genus; OTU Figure 5. Taxa specific associations with growth. The figure shows negative and positive associations of specific taxa with changes in z-scores between 6 and 12 mo: LAZ, length-for-age z score; WAZ, weight-for-age z score; and WLZ, weight-for-length z score. The negative associations that were significant only before FDR correction (at 15%) and those that remained significant after correction are shown in pink and dark red, respectively. Positive associations that were significant only before correction are shown in light green colour, while those that remained significant after correction are in dark green colour. 9 Scientific RepoRtS | (2019) 9:12893 | https://doi.org/10.1038/s41598-019-49274-y www.nature.com/scientificreports www.nature.com/scientificreports/ is possible that inflammation at 6 mo may have been chronic (i.e pre-existing from early infancy) and thus that inflammation affected microbial composition rather than vice versa. At 18 mo however, a concurrent inverse relationship was observed between microbiota characteristics and CRP, though not AGP. Raised CRP levels are associated with recent infection with or without clinical evidence of disease32. The percentage of children with high CRP values was elevated among those with MAZ or Shannon index values below the median (compared with those above the median), by 6 and 12 percentage points, respectively. This suggests that a more mature and diverse microbiota community may help to prevent inflammation, but we cannot rule out the possibility that the relationship works in the opposite direction, i.e., that inflammation affects microbial diversity. Previous studies in adults demonstrated that lower alpha diversity and gene count of the gut microbiome were associated with higher levels of high sensitivity CRP4,33. High sensitivity CRP is an inflammatory biomarker for myocardial infarction, stroke and peripheral arterial diseases34. Mucosal inflammation in inflammatory bowel disease (IBD) has been associated with a significant reduction in the diversity of the gut microbiota measured by Shannon index35–37. Although determining whether variations in microbiota composition contribute to inflammation is a challenge in humans, it has recently been demonstrated in mice that a reduction in airway microbiota diversity was associated with elevated allergic respiratory inflammation38, suggesting that an altered microbiota profile can affect inflammation. At 6 mo, several taxa were positively associated with AGP concentration including Ruminococcus gnavus, Lactobacillus and Clostridiales. Previous studies have reported an enrichment of Ruminococcus gnavus and Lactobacillus in children with IBD, which is consistent with our findings, while Clostridiales were depleted in individuals with IBD relative to healthy controls9,39–41, which is in conflict with our results. Several taxa present at 18 mo were positively associated with concurrent inflammation including Prevotella, Clostridium innocuum and Leuconostoc. While Prevotella may be beneficial because of its cellulolytic activities, it has also been linked to chronic inflammatory conditions such as arthritis as well as mucosal and systemic T-cell activation in HIV infected subjects not on therapy42. Because Prevotella is a genus comprised of many different species, it is possible Bivariate LAZWAZ WLZ Firmicutes; Veillonella; OTU4388775 Firmicutes; Veillonella; OTU4422456 Firmicutes; Veillonella; OTU4318671 Firmicutes; und; OTU128382 Firmicutes; Erysipelotrichaceae; OTU182483 Firmicutes; Erysipelotrichaceae; OTU179018 Firmicutes; Ruminococcus; OTU184238 Firmicutes; Faecalibacterium; OTU181422 Firmicutes; Faecalibacterium; OTU4381430 Firmicutes; Faecalibacterium; OTU265871 Firmicutes; Faecalibacterium; OTU193644 Firmicutes; Faecalibacterium; OTU189092 Firmicutes; Faecalibacterium; OTU188676 Firmicutes; Faecalibacterium; OTU186772 Firmicutes; Faecalibacterium; OTU199293 Firmicutes; Faecalibacterium; OTU193873 Firmicutes; und; OTU195436 Firmicutes; Roseburia; OTU3924627 Firmicutes; Eubacterium; OTU180999 Firmicutes; Eubacterium; OTU3609545 Firmicutes; Eubacterium; OTU192406 Firmicutes; Eubacterium; OTU4447605 Firmicutes; Clostridium; OTU4445673 Firmicutes; Clostridium; OTU170652 Firmicutes; Clostridium; OTU3576174 Firmicutes; Clostridium; OTU606927 Firmicutes; Clostridium; OTU4331360 Firmicutes; Clostridium; OTU182289 Firmicutes; und; OTU190502 Firmicutes; und; OTU186732 Firmicutes; und; OTU179267 Firmicutes; und; OTU176980 Firmicutes; Streptococcus; OTU4307484 Firmicutes; Streptococcus; OTU1079708 Firmicutes; Streptococcus; OTU4442130 Firmicutes; Streptococcus; OTU1059655 Firmicutes; Streptococcus; OTU300658 Firmicutes; Lactococcus; OTU1100972 Firmicutes; Weissella; OTU663969 Firmicutes; Leuconostoc; OTU4365141 Firmicutes; Pediococcus; OTU773251 Firmicutes; Lactobacillus; OTU4463108 Firmicutes; Lactobacillus; OTU4414476 Firmicutes; Lactobacillus; OTU4305372 Firmicutes; Lactobacillus; OTU533133 Firmicutes; Lactobacillus; OTU318764 Firmicutes; Enterococcus; OTU3697034 Firmicutes; Enterococcus; OTU810399 Firmicutes; und; OTU3134492 Phylum; Genus; OTU Figure 6. Taxa specific associations with growth, for Firmicutes phylum only. The figure shows negative and positive association of specific taxa changes in z-scores between 6 and 12 mo: LAZ, length-for-age z score; WAZ, weight-for-age z score; and WLZ, weight-for-length z score. The negative associations that were significant only before FDR correction at 15% and those that remained significant after correction are shown in pink and dark red, respectively. Positive associations that were significant only before correction are shown in light green color, while those that remained significant after correction are in dark green color.